Selected Cell
Cell:
Value:
starting_page
variables
value_sets
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
| Variable mit reduziertem DD überarbeiten (SM) | überarbeiten | IF APPLICABLE | IF APPLICABLE | IF APPLICABLE | IF APPLICABLE | IF APPLICABLE | IF APPLICABLE | ||||||||||
| mit reduziertem DD überarbeitet | übertragen | # mandatory (es darf kein NA geben) # conditional: {variable} = value OR [variable] ∈ {value₁, value₂, …} OR at least one of {variable1, variable2,...} = value # context-dependent (e.g. if applicable depending on study or variable characteristics) | # date (always format YYYY-MM-DD or YYYY-MM) # integer (numeric; no decimal places) # float (numeric; decimal places) # string (text) # boolean (true/false) # hyperlink | # continous # discrete (counting discrete units, intervals equal and meaningful, e.g. number of participants, number of adverse events) # ordinal (categories/ranks, intervals uequal/undefined, e.g. Likert scales, educational level) # nominal # binary | # Numeric ranges are indicated using interval notation [min, max] # Discrete values and categories are indicated using set notation {…} # integer (if applicable): X digits | e.g. days/years/months, percentage | if field type = float | autocalculated variables | Link/Name of documents further explaining or additionally describing the content of the variable | ||||||||
| variable_name | inclusion_database_v01 | inclusion_database_v03 | table | section | definition | coding_rules | applicability | field_type | measurement_level | prompt_type | value_set_or_range | unit | decimals | autocalc_from | example | status | supporting_document |
| report_num | outdated version | lev0_reports | identity | Stable numeric identifier for each included report. | Assign sequentially when the report enters ResiMETA. Use the same value for all linked records. Never renumber and never reuse retired numbers. | mandatory | integer | nominal | input field | [1, ∞] | NA | NA | NA | 14 | active | NA | |
| report_id | outdated version | lev0_reports | identity | Human-readable identifier for an included report. | Format: Surname_YYYY_R#### # Surname: normalized first-author surname. Retain hyphens that are part of the surname (e.g., `Garcia-Rudolph`). Replace spaces in compound surnames with hyphens (e.g., `Brennan Kearns` → `Brennan-Kearns`). Transliterate non-ASCII characters to their closest ASCII equivalent (e.g., `Bórquez` → `Borquez`, `Mørk` → `Mork`, `Pyykkö` → `Pyykko`). Remove other punctuation (e.g., `O'Donnell` → `ODonnell`) # YYYY: publication/dissemination year # R####: zero-padded report_num with prefix R (e.g., report_num = 438 → R0438) Do not encode sample identity or report type. Preserve the original author spelling in bibliographic metadata. Bibliographic components may be corrected if metadata are found to be erroneous, but report_num/R#### never changes. | mandatory | string | nominal | input field | NA | NA | NA | NA | Van-Leeuwen_2012_R0349 | active | NA | |
| report_data_type | yes | lev0_reports | extraction | Type of resiMETA data extracted from the report. | Classify whether the report contributes trajectory data, resilience-factor data, or both. | mandatory | string | nominal | formula | vs_report_data_type | NA | NA | pending | TRAJ_RF | derived | NA | |
| report_data_retained | yes | lev0_reports | extraction | Type of report data retained for the current primary-analysis dataset after overlap/selection rules. | Derive from retained trajectory-model and RF rows | context-dependent | string | nominal | formula | vs_report_data_retained | NA | NA | traj_model_sample_overlap_primary, rf_sample_overlap_primary | TRAJ | derived | NA | |
| report_pid | yes | lev0_reports | bibliography | Persistent identifier for the report. | Enter a resolvable DOI URL when available; otherwise use a resolvable Handle or repository URL. | context-dependent | hyperlink | nominal | input field | NA | NA | NA | NA | https://doi.org/10.5271/sjweh.3889 | active | NA | |
| report_year | yes | lev0_reports | bibliography | Publication or dissemination year of the report. | For journal articles, use issue/print year rather than online-first year when they differ. | mandatory | integer | discrete | input field | YYYY | year | NA | NA | 2020 | active | NA | |
| report_citation | yes | lev0_reports | bibliography | Full bibliographic citation for the report. | Use current APA style and include a DOI URL when available. | mandatory | string | nominal | input field | NA | NA | NA | NA | Åhlin, J. K., Peristera, P., Westerlund, H., & Magnusson Hanson, L. L. (2020). Psychosocial working characteristics before retirement and depressive symptoms across the retirement transition: A longitudinal latent class analysis. Scandinavian Journal of Work, Environment & Health, 46(5), 488-497. https://doi.org/10.5271/sjweh.3889 | active | NA | |
| report_type | no | lev0_reports | bibliography | Formal dissemination type of the included report. | Code the document actually included as the report record, i.e. the highest formal publication level available for this record. | mandatory | string | nominal | drop-down list (single) | vs_report_type | NA | NA | NA | peer_reviewed_article | active | NA | |
| report_preregistration_url | no | lev0_reports | bibliography | URL of an a-priori preregistration relevant to the investigation or analysis represented by the report. | Include only an official preregistration specifying a-priori aims, design, or analysis plans. Typical official registry identifiers are OSF, ClinicalTrials.gov, ANZCTR. Exclude ethics approvals, cohort registrations, and non-preregistered protocols. | context-dependent | hyperlink | nominal | input field | NA | NA | NA | NA | https://clinicaltrials.gov/study/NCT04366154 | active | NA | |
| report_inclusion_wave | yes | lev0_reports | workflow | Search-update wave in which the report entered resiMETA | Store as YYYY-MM. | mandatory | date | continuous | input field | YYYY-MM | NA | NA | NA | 2023-08 | active | NA | |
| report_author_contact_status | yes | lev0_reports | workflow | Status of author contact for clarification of report methods or reported data. | Update as clarification proceeds. | mandatory | string | nominal | drop-down list (single) | vs_author_contact_status | NA | NA | NA | answer_received | active | pending | |
| report_author_contact_desc | no | lev0_reports | workflow | Brief description of a clarification request and, where relevant, the received answer. | Use only when contact is needed or has occurred; do not duplicate structured fields. | conditional: report_author_contact_status != no_need | string | nominal | input field | NA | NA | NA | NA | clarified whether class proportions were model-estimated | active | pending | |
| report_comments | no | lev0_reports | workflow | Free-text notes on report selection and extraction decisions. | Use sparingly for report-level exceptions, additional sources, or deviations not captured in structured fields. | context-dependent | string | nominal | input field | NA | NA | NA | NA | conference abstract superseded by full article | active | NA | |
| report_sample_id | no | lev1_report_samples | identity | Stable identifier for one analytic sample as represented in one report. | mandatory | string | nominal | input field | NA | NA | NA | NA | rsamp_0438_01 | active | NA | ||
| report_multiple_samples | yes | lev1_report_samples | identity | Indicates whether the report contains more than one distinct report-sample record. | TRUE when the same report_num occurs in more than one report_samples row with different report_sample_id values. Otherwise FALSE. | mandatory | boolean | binary | formula | vs_boolean | NA | NA | NA | false | derived | NA | |
| report_multiple_samples_desc | yes | lev1_report_samples | identity | Feature that distinguishes this analytic sample from other analytic samples represented in the same report. | Describe only the distinguishing feature, e.g. women vs men, intervention vs control, country A vs country B. Do not repeat general sample characteristics. | conditional: report_multiple_samples = TRUE | string | nominal | input field | NA | NA | NA | NA | women | active | NA | |
| sample_id | no | lev1_report_samples | identity | Stable identifier for the same intended analytic participant sample across reports. | Format: samp_####_##. With `####_##` derived from the report_sample_id of the first ResiMETA report in which the intended sample appears as anchor. Reuse across reports when the intended participants are the same despite differences in outcome, model, time window, wave count, missingness/completeness requirements, or analysis-specific sample size. Assign a new sample_id for substantively different samples, including substantively defined nested subsamples drawn from a broader cohort (e.g., accident-only subset of a mixed-trauma cohort, women-only subgroup, treatment-defined subgroup). Also assign a new sample_id when separate analyses are explicitly based on different participant sets, even if these differences arise from measurement availability or timing (e.g., two biomarker groups measured at different timepoints that consist largely of different participants). Do not assign a new sample_id when a smaller analytic N results only from attrition, missing data, outcome availability, minimum-wave requirements, or other analysis-specific completeness criteria and does not define a separate analytic participant group. Also assign a new sample_id when samples come from the same site, hospital, registry, cohort, or study centre but cannot be assumed to include the same participants or are defined by different substantive eligibility criteria. Link related but non-identical samples (e.g., women and men from the same cohort study) through sample_overlap_group_id. | mandatory | string | nominal | input field | NA | NA | NA | NA | samp_0438_01 | active | Sample overlap rules | |
| sample_overlap | no | lev1_report_samples | sample dependency | Indicates membership in a documented sample-dependency group. | Derive TRUE when sample_overlap_group_id is present and links this record to at least one other included report-sample; FALSE otherwise. Dependency groups may contain identical, overlapping, nested, uncertain, or known-disjoint samples from a shared source family. | mandatory | boolean | binary | drop-down list (single) | vs_boolean | NA | NA | sample_overlap_group_id | true | active | Sample overlap rules | |
| sample_overlap_group_id | no | lev1_report_samples | sample dependency | Identifier linking report-samples that belong to the same sample-dependency family. | Format: ovlp_####_##. With ####_## derived from the report_sample_id of the first resiMETA report in which the intended sample appears as anchor for the dependency family. The same group may contain repeated same-sample records (i.e., same sample_id and the same sample_overlap_group_id) and related but non-identical samples (i.e., different sample_ids but same sample_overlap_group_id). | conditional: sample_overlap = TRUE | string | nominal | input field | NA | NA | NA | NA | ovlp_0438_01 | active | Sample overlap rules | |
| sample_overlap_category | outdated version | lev1_report_samples | sample dependency | Group-level classification of the relationship among report-samples sharing a sample_overlap_group_id. | Assign one category to the entire group and repeat it on all member rows. Use mixed only when the group contains substantively different non-identical relationships that cannot be represented by one simpler category. | conditional: sample_overlap = TRUE | string | nominal | input field | vs_sample_overlap_category | NA | NA | NA | nested | active | Sample overlap rules | |
| sample_overlap_same_sample_diff | outdated version | lev1_report_samples | sample dependency | Analytic features that differ between report-samples sharing the same sample_id. | Apply when at least two report-sample rows in the dependency family share the same sample_id, including within nested or mixed groups. Multiple tags allowed; store in fixed order separated by ', ': outcome; rf; timepoints; model; analytic_sample; other. | context-dependent | string | nominal | drop-down list (multiple) | vs_sample_overlap_same_sample_diff | NA | NA | NA | outcome, timepoints | active | Sample overlap rules | |
| sample_overlap_primary | no | lev1_report_samples | selection | indication whether the study is selected as the primary report for main analyses among a set of overlapping reports sharing a {sample_overlap_group_id} | # copy from supporting document | mandatory | integer | nominal | drop-down list (single) | [0, 1, 2] | NA | NA | NA | 1 | |||
| outc_total_num | yes | lev1_report_samples | derived summaries | Number of distinct eligible outcome constructs modeled for this report-sample. | Count distinct standardized outcome constructs across linked trajectory models. | mandatory | integer | discrete | formula | [0, ∞] | NA | NA | lev2_trajectory_models.outc_label | 2 | derived | NA | |
| outc_total_labels | yes | lev1_report_samples | derived summaries | Standardized labels of distinct eligible outcomes modeled for this report-sample. | Derive unique labels from linked trajectory models; store in deterministic alphabetical order separated by ', '. | mandatory | string | nominal | formula | NA | NA | NA | lev2_trajectory_models.outc_label | anxiety_symptoms, depressive_symptoms | derived | NA | |
| rf_total | yes | lev1_report_samples | derived summaries | Indicates whether at least one eligible resilience factor is linked to this report-sample. | TRUE when at least one linked resilience_factors row is present. | mandatory | boolean | binary | formula | vs_boolean | NA | NA | lev3_resilience_factors | true | derived | NA | |
| rf_total_num | yes | lev1_report_samples | derived summaries | Number of distinct resilience-factor constructs linked to this report-sample. | Count distinct rf_label values; use 0 when none. | mandatory | integer | discrete | formula | [0, ∞] | NA | NA | lev3_resilience_factors.rf_label | 2 | derived | NA | |
| rf_total_labels | yes | lev1_report_samples | derived summaries | Standardized labels of distinct resilience factors linked to this report-sample. | Derive unique labels from linked RF rows; store in deterministic order separated by ', '. | conditional: rf_total = TRUE | string | nominal | formula | NA | NA | NA | lev3_resilience_factors.rf_label | perceived_social_support, relationship_satisfaction | derived | NA | |
| report_sample_nos_score | no | lev1_report_samples | quality | Methodological quality score for the report-sample record using the modified Newcastle-Ottawa Scale. | Represents the most conservative methodological quality rating across all trajectory-model/outcome records belonging to the same report_sample_id. Derive the lowest observed traj_model_nos_score within the report-sample. If only one trajectory-model record exists, copy its score. | context-dependent | float | continuous | formula | [0.00, 100.00] | percent | 2 | lev2_trajectory_models.traj_model_nos_score | 66.67 | active | NOS Table | |
| sample_country | outdated version | lev1_report_samples | geography | Country where the analytic sample was collected. | Use the official ISO 3166-1 short country name. Normalize UK, Great Britain, England, Scotland, Wales, Northern Ireland to United Kingdom. For samples spanning multiple countries, code NA here and use sample_multinational_list. | context-dependent | string | nominal | drop-down list (single) | ISO 3166‑1 short country names | NA | NA | NA | Sweden | active | NA | |
| study_multinational | no | lev1_report_samples | geography | Indicates whether the analytic sample includes data collected in two or more countries. | Code TRUE if data were collected in ≥2 countries, otherwise FALSE. Multicenter sampling within one country is FALSE. | mandatory | boolean | binary | drop-down list (single) | vs_boolean | NA | NA | NA | true | active | NA | |
| study_multinational_list | no | lev1_report_samples | geography | Countries represented in a multinational analytic sample. | Use the official ISO 3166-1 short country names separated by ', '. | conditional: sample_multinational = TRUE | string | nominal | input field | ISO 3166‑1 short country names | NA | NA | NA | Côte d’Ivoire, Ghana | active | NA | |
| sample_country_iso3 | no | lev1_report_samples | geography | ISO 3166-1 alpha-3 code for sample_country. | Derive from the maintained country reference table. NA for multinational samples without one single country. | context-dependent | string | nominal | input field | ISO 3166‑1 alpha‑3 | NA | NA | study_country_info_auto_update.iso3c | SWE | derived | Country-context reference table | |
| sample_country_unsd_m49 | no | lev1_report_samples | geography | UNSD M49 country code for {study_country} | # update regulary | {study_country_info_auto_update.unsd_m49_code} | |||||||||||
| sample_continent | no | lev1_report_samples | geography | UNSD M49 continental region of the sample country or countries. | Derive from the maintained country reference table. If a multinational sample spans different continents, code NA. CAVE: manually check for multinational studies if all countries belong to the same continent | context-dependent | string | nominal | input field | NA | NA | NA | study_country_info_auto_update.continent_unsd | Africa | derived | Country-context reference table | |
| sample_continent_subregion | no | lev1_report_samples | geography | UNSD M49 subregion of the sample country or countries. | Derive from the maintained country reference table. If a multinational sample spans different subregions, code NA. CAVE: manually check for multinational studies if all countries belong to the same subregion | context-dependent | string | nominal | input field | NA | NA | NA | study_country_info_auto_update.subregion_unsd | Northern Africa | derived | Country-context reference table | |
| country_income_group | yes | lev1_report_samples | country context | World Bank income group of the sample country. | Derive from the maintained country-context reference table. | context-dependent | string | ordinal | input field | NA. | NA | NA | {study_country_info_auto_update.income_group_wb} | High income | derived | NA | |
| country_gini_index_score | yes | lev1_report_samples | country context | Gini index score; higher values indicate greater income inequality. | Derive from the maintained country-context reference table. | context-dependent | float | continuous | input field | [0, 100] | percentage | 1 | {study_country_info_auto_update.gini_wb_most_recent} | 29.8 | derived | NA | |
| country_gdp_score | yes | lev1_report_samples | country context | Gross domestic product in current U.S. dollars for the designated reference year. | Derive from the maintained country-context reference table. | context-dependent | integer | continuous | input field | [0, ∞] | USD | NA | {study_country_info_auto_update.gdp_wb_most_recent} | 5.41487E11 | derived | NA | |
| country_hdi_score | yes | lev1_report_samples | country context | Human Development Index score for the designated reference year. | Derive from the maintained country-context reference table. | context-dependent | float | continuous | input field | [0, 1] | NA | 3 | {study_country_info_manual_update.hdi_score} | 0.952 | derived | NA | |
| country_education_index_adjusted_score | yes | lev1_report_samples | country context | Inequality-adjusted education index score for the designated reference year. | Derive from the maintained country-context reference table. | context-dependent | float | continuous | input field | [0, 1] | NA | 3 | {study_country_info_manual_update.education_index_adjusted_score} | 0.891 | derived | NA | |
| country_cip_score | yes | lev1_report_samples | country context | Competitive Industrial Performance index score for the designated reference year. | Derive from the maintained country-context reference table. | context-dependent | float | continuous | input field | [0, 1] | NA | 3 | {study_country_info_manual_update.cip_index_score} | 0.116 | derived | NA | |
| country_cip_rank | yes | lev1_report_samples | country context | Competitive Industrial Performance rank for the designated reference year. | Derive from the maintained country-context reference table. | context-dependent | integer | ordinal | input field | [1, ∞] | NA | NA | {study_country_info_manual_update.cip_index_rank} | 16 | derived | NA | |
| country_democracy_index_score | yes | lev1_report_samples | country context | Economist Intelligence Unit Democracy Index score for the designated reference year. | Derive from the maintained country-context reference table. | context-dependent | float | continuous | input field | [0, 10] | NA | 2 | {study_country_info_manual_update.democracy_index_score} | 9.26 | derived | NA | |
| country_democracy_index_rank | yes | lev1_report_samples | country context | Rank based on the Democracy Index score for the designated reference year. | Derive from the maintained country-context reference table. | context-dependent | integer | ordinal | input field | [1, ∞] | NA | NA | {study_country_info_manual_update.democracy_index_rank} | 3 | derived | NA | |
| country_gci_score | yes | lev1_report_samples | country context | Global Collectivism Index score; higher values indicate greater collectivism. | Derive from the maintained country-context reference table. | context-dependent | float | continuous | input field | NA | NA | 2 | {study_country_info_stable.gci_score} | -1.43 | derived | NA | |
| country_hofstede_pdi | yes | lev1_report_samples | country context | Hofstede power-distance score for the sample country. | Derive from the maintained country-context reference table. | context-dependent | integer | ordinal | input field | [0, ∞] | NA | NA | {study_country_info_stable.hofstede_pdi} | 31 | derived | Country-context reference table | |
| country_hofstede_idv | yes | lev1_report_samples | country context | Hofstede individualism score for the sample country. | Derive from the maintained country-context reference table. | context-dependent | integer | ordinal | input field | [0, ∞] | NA | NA | {study_country_info_stable.hofstede_idv} | 71 | derived | Country-context reference table | |
| country_hofstede_mas | yes | lev1_report_samples | country context | Hofstede masculinity score for the sample country. | Derive from the maintained country-context reference table. | context-dependent | integer | ordinal | input field | [0, ∞] | NA | NA | {study_country_info_stable.hofstede_mas} | 5 | derived | Country-context reference table | |
| country_hofstede_uai | yes | lev1_report_samples | country context | Hofstede uncertainty-avoidance score for the sample country. | Derive from the maintained country-context reference table. | context-dependent | integer | ordinal | input field | [0, ∞] | NA | NA | {study_country_info_stable.hofstede_uai} | 29 | derived | Country-context reference table | |
| country_hofstede_lto | yes | lev1_report_samples | country context | Hofstede long-term-orientation score for the sample country. | Derive from the maintained country-context reference table. | context-dependent | integer | ordinal | input field | [0, ∞] | NA | NA | {study_country_info_stable.hofstede_lto} | 53 | derived | Country-context reference table | |
| country_hofstede_ivr | yes | lev1_report_samples | country context | Hofstede indulgence score for the sample country. | Derive from the maintained country-context reference table. | context-dependent | integer | ordinal | input field | [0, ∞] | NA | NA | {study_country_info_stable.hofstede_ivr} | 78 | derived | Country-context reference table | |
| sample_pop_type | yes | lev1_report_samples | analytic sample | Broad classification of the analytic sample recruitment context and setting. | Apply the population-category supporting manual to the modeled analytic sample only. | mandatory | string | nominal | drop-down list (single) | vs_sample_pop_type | NA | NA | NA | student_population | active | ResiMETA_Coding_Manual_Population_Categories | |
| sample_pop_type_mixed | yes | lev1_report_samples | analytic sample | Applicable population-type categories when sample_pop_type = mixed. | Use canonical categories only. Store multiple values separated by ', '. | conditional: sample_pop_type = mixed | string | nominal | drop-down list (multiple) | vs_sample_pop_type_mixed | NA | NA | NA | clinical_population, student_population | active | ResiMETA_Coding_Manual_Population_Categories | |
| sample_pop_structural_institutional | outdated version | lev1_report_samples | analytic sample | Specific subgroup classification for this population context. | Select all applicable canonical categories according to the population-category manual. Store multiple values separated by ', '. | conditional: {sample_pop_type, sample_pop_type_mixed} = structural_or_institutional_context_population | string | nominal | drop-down list (multiple) | vs_sample_pop_structural_institutional | NA | NA | NA | humanitarian_migrants | active | ResiMETA_Coding_Manual_Population_Categories | |
| sample_pop_role_status_life_stage | outdated version | lev1_report_samples | analytic sample | Specific subgroup classification for this population context. | Select all applicable canonical categories according to the population-category manual. Store multiple values separated by ', '. | conditional: {sample_pop_type, sample_pop_type_mixed} = role_status_or_life_stage_population | string | nominal | drop-down list (multiple) | vs_sample_pop_role_status_life_stage | NA | NA | NA | retirees | active | ResiMETA_Coding_Manual_Population_Categories | |
| sample_pop_working | yes | lev1_report_samples | analytic sample | Specific subgroup classification for this population context. | Select all applicable canonical categories according to the population-category manual. Store multiple values separated by ', '. | conditional: {sample_pop_type, sample_pop_type_mixed} = working_population | string | nominal | drop-down list (multiple) | vs_sample_pop_working | NA | NA | NA | military_personnel | active | ResiMETA_Coding_Manual_Population_Categories | |
| sample_pop_student | yes | lev1_report_samples | analytic sample | Specific subgroup classification for this population context. | Select all applicable canonical categories according to the population-category manual. Store multiple values separated by ', '. | conditional: {sample_pop_type, sample_pop_type_mixed} student_population | string | nominal | drop-down list (multiple) | vs_sample_pop_student | NA | NA | NA | college_or_university_students | active | ResiMETA_Coding_Manual_Population_Categories | |
| sample_pop_clinical | yes | lev1_report_samples | analytic sample | Specific subgroup classification for this population context. | Select all applicable canonical categories according to the population-category manual. Store multiple values separated by ', '. | conditional: {sample_pop_type, sample_pop_type_mixed} clinical_population | string | nominal | drop-down list (multiple) | vs_sample_pop_clinical | NA | NA | NA | physical_health_condition | active | ResiMETA_Coding_Manual_Population_Categories | |
| sample_inclusion_criteria | no | lev1_report_samples | analytic sample | Inclusion criteria defining the analytic sample. | Use verbatim or near-verbatim wording. Write criteria as numbered items within one cell, e.g., "1) …, 2) ..., …". Expand non-standard author abbreviations. | mandatory | string | nominal | input field | NA | NA | NA | NA | 1) observable retirement transition (= going from paid work in one wave [completed the questionnaire for ‘workers’] to being retired [completed a questionnaire for ‘non-workers’ and reported being retired] in the following wave during waves 2–6, 2) old age retirees or another type of pension (eg., contractual pension) on a full-time basis | active | NA | |
| sample_exclusion_criteria | no | lev1_report_samples | analytic sample | Exclusion criteria defining the analytic sample. | Use verbatim or near-verbatim wording. Write criteria as numbered items within one cell, e.g., "1) …, 2) ..., …". Expand non-standard author abbreviations. | mandatory | string | nominal | input field | NA | NA | NA | NA | 1) retirement due to ill-health, 2) reported transitions from retirement back to paid work (unretirement), 3) not reported depressive symptoms in ≥4 waves | active | NA | |
| sample_desc | yes | lev1_report_samples | analytic sample | Brief description of the analytic sample. | Focus on recruitment context and defining characteristics; exclude sample size, country, and stressor intensity (captured elsewhere). | mandatory | string | nominal | input field | NA | NA | NA | NA | retirees with an observable retirement transition | active | NA | |
| sample_parent_dataset | no | lev1_report_samples | parent dataset | Indicates whether the analytic sample was drawn from a pre-existing parent dataset, cohort, panel, registry, or survey. | TRUE for secondary use of an established data infrastructure, that is, data were not originally collected specifically for the reported analyses (e.g., established cohort, registry, panel, secondary analysis). FALSE when data were collected specifically for the reported investigation. | mandatory | boolean | binary | drop-down list (single) | vs_boolean | NA | NA | NA | true | active | NA | |
| sample_parent_dataset_name | yes | lev1_report_samples | parent dataset | Official name of the parent dataset. | Use the official name reported by authors. If a secondary analysis is reported but no official parent dataset name is specified, code NA. | conditional: sample_parent_dataset = TRUE | string | nominal | input field | NA | NA | NA | NA | Swedish Longitudinal Occupational Survey of Health | active | NA | |
| sample_parent_dataset_abbr | no | lev1_report_samples | parent dataset | Official abbreviation of the parent dataset. | Use only an officially reported abbreviation, otherwise NA. | conditional: sample_parent_dataset = TRUE | string | nominal | input field | NA | NA | NA | NA | SLOSH | active | NA | |
| sample_parent_dataset_desc | yes | lev1_report_samples | parent dataset | Brief description of the parent dataset relevant to sample provenance. | Prefer concise near-verbatim wording. 1–3 sentences maximum. | conditional: sample_parent_dataset = TRUE | string | nominal | input field | NA | NA | NA | NA | a cohort survey of individuals aged 16–64 years from across the entire country and fairly representative of the Swedish working population | active | NA | |
| sample_treatment | no | lev1_report_samples | analytic sample | Indicates whether the analytic sample received a structured mental-health intervention during the modeled observation window. | TRUE if at least a part of the sample receives protocolized psychological, psychiatric, psychosocial, prevention/resilience, or protocol-based psychopharmacological intervention with mental-health intent (e.g., antidepressant medication arm / supervised medication algorithm). FALSE for no treatment or only informal or unsystematic support or purely medical treatments without psychological components. Special cases # Routine clinical care or treatment-as-usual (e.g., participants receiving standard psychiatric treatment, medication prescribed by treating clinicians, inpatient care) is FALSE unless the treatment is protocolized, assigned, or analytically modeled as part of the study design. # RCT: intervention group only, or pooled intervention/control = TRUE; control group only = FALSE # psychopharmacological treatment specificity: TRUE only if it is protocol-based and methodologically specified (as part of study methods) AND occurs during the modeled observation window. FALSE if medication use is mentioned only in sample descriptives (e.g., “x% taking antidepressants”). | mandatory | boolean | binary | drop-down list (single) | vs_boolean | NA | NA | NA | true | active | NA | |
| sample_treatment_desc | no | lev1_report_samples | analytic sample | Brief description of the structured intervention. | Specify intervention type and core content (e.g., CBT, group resilience training, stepped-care); prefer concise near-verbatim wording. If treatment is mentioned but not described, code NA. | conditional: sample_treatment = TRUE | string | nominal | input field | NA | NA | NA | NA | group resilience training | active | NA | |
| sample_treatment_partly | no | lev1_report_samples | analytic sample | Indicates whether only a subset of the analytic sample received the structured intervention. | TRUE when the modeled sample combines intervention-exposed and unexposed participants. FALSE when all modeled participants received it. | conditional: sample_treatment = TRUE | boolean | binary | drop-down list (single) | vs_boolean | NA | NA | NA | true | active | NA | |
| sample_female_pct | yes | lev1_report_samples | demographics | Percentage of participants categorized as female in the analytic sample. | Extract as reported. If derivable from female n and total analytic N, calculate. Retain reported decimal precision; self-calculated values use two decimals. | mandatory | float | continuous | input field | [0, 100] | percent | variable | NA | 54.3 | active | NA | |
| sample_age_years_mean | yes | lev1_report_samples | demographics | Mean age of the analytic sample in years. | Prefer the first modeled wave. Extract as reported (retain decimal places). Convert to years if necessary and round to two decimal places. Special cases # If only class-specific means are available, calculate a weighted overall mean when defensible (e.g. by using the following online-tool: https://www.statstodo.com/CombineMeansSDs.php) and document the derivation in sample_comments. # If age can only be inferred from the measurement design (e.g., cohort defined by age at each wave), the corresponding value may be recorded as the sample mean age. Prefer the age corresponding to the first measurement wave used in the trajectory model; if this cannot be clearly identified, use the earliest modeled wave. Document the data source in sample_comments (e.g., "age inferred from measurement design) | mandatory | float | continuous | input field | [0, ∞] | years | variable | NA | 65.9 | active | NA | |
| sample_age_years_sd | yes | lev1_report_samples | demographics | Standard deviation of age in years for the analytic sample. | Prefer the first modeled wave. Extract as reported (retain decimal places). Convert to years if necessary and round to two decimal places. Special cases # If only class-specific standard deviations are available, calculate a pooled standard deviation when defensible (e.g. by using the following online-tool: https://www.statstodo.com/CombineMeansSDs.php) and document the derivation in sample_comments. | mandatory | float | continuous | input field | [0, ∞] | years | variable | NA | 8.1 | active | NA | |
| sample_age_years_min | yes | lev1_report_samples | demographics | Minimum observed age in years. | Prefer the first modeled wave. Extract as reported (retain decimal places). Convert to years if necessary and round to two decimal places. Use the observed sample range, not eligibility limits. | context-dependent | float | continuous | input field | [0, ∞] | years | variable | NA | 55 | active | NA | |
| sample_age_years_max | yes | lev1_report_samples | demographics | Maximum observed age in years. | Prefer the first modeled wave. Extract as reported (retain decimal places). Convert to years if necessary and round to two decimal places. Use the observed sample range, not eligibility limits. | context-dependent | float | continuous | input field | [0, ∞] | years | variable | NA | 74 | active | NA | |
| sample_age_years_other | yes | lev1_report_samples | demographics | Alternative age statistic when mean and SD are unavailable. | Prefer the first modeled wave. Extract as reported (retain decimal places). Convert to years if necessary and round to two decimal places. Prefix the statistic, e.g. "median (IQR): 28 (23-30)". Code NA when mean and SD are available. | conditional: sample_age_years_mean = NA | string | nominal | input field | NA | NA | NA | NA | median (IQR): 28 (23-30) | active | NA | |
| sample_age_categories | yes | lev1_report_samples | demographics | Age-category counts and/or percentages as reported. | Format "category: n (pct), category: n (pct)". Convert age cut points to years when needed. | context-dependent | string | nominal | input field | NA | NA | NA | NA | <25: 96 (15.61), ≥25: 519 (84.39) | active | NA | |
| sample_age_group | yes | lev1_report_samples | demographics | Standardized age-group classification of the analytic sample. | Categories: young = 0–24 years; middle = 25–60 years; old = >60 years. Use observed range first; if unavailable use mean ± 1 SD. Multiple groups may apply. | mandatory | string | nominal | drop-down list (multiple) | vs_sample_age_group | NA | NA | NA | middle, old | active | NA | |
| sample_youth | no | lev1_report_samples | demographics | Indicates whether the analytic sample is predominantly 0–24 years old. | TRUE if sample_age_group = young, or mean + SD ≤ 24.99, or ≥ 80% of the sample is 0–24 years old. NA if age information is insufficient. | context-dependent | boolean | binary | drop-down list (single) | vs_boolean | NA | NA | NA | true | active | NA | |
| sample_comments | outdated version | lev1_report_samples | demographics | Free-text notes on analytic-sample extraction or special cases. | Document uncertainty, use of non-analytic-sample statistics, inferred timing, or other exceptions not captured in structured fields. | context-dependent | string | nominal | input field | NA | NA | NA | NA | age inferred from measurement design | active | NA | |
| stressor_name | outdated version | lev1_report_samples | index stressor | Author wording for the resiMETA index stressor defining the analytic sample or trajectory time anchor. | Extract exactly one index stressor using the project hierarchy. Prefer verbatim or near-verbatim wording; standardize 'COVID-19 pandemic' where applicable. | mandatory | string | nominal | input field | NA | NA | NA | NA | COVID-19 pandemic | active | ResiMETA_Coding_Manual_Stressor_Identification_and_Categories | |
| stressor_category | outdated version | lev1_report_samples | index stressor | Standardized category of the index stressor. | Apply the stressor-category manual to stressor_name. | mandatory | string | nominal | drop-down list (single) | vs_stressor_category | NA | NA | NA | pandemics_and_epidemics | active | ResiMETA_Coding_Manual_Stressor_Identification_and_Categories | |
| stressor_perinatal_spec | no | lev1_report_samples | index stressor | Standardized specification for perinatal stressors. | Apply the stressor-category manual to stressor_name. | conditional: stressor_category = perinatal_stressors | string | nominal | drop-down list (single) | vs_stressor_perinatal_spec | NA | NA | NA | pregnancy | active | ResiMETA_Coding_Manual_Stressor_Identification_and_Categories | |
| stressor_level | no | lev1_report_samples | index stressor | Primary level at which the index stressor is generated or anchored. | Code the origin/anchor of the stressor, not the level at which its effects occur (e.g., own cancer diagnosis = individual; partner cancer diagnosis/caregiving = social; pandemic context = societal). Aply the project stressor categoriesl; apply case-specific override rules where specified. Use mixed only when pooled index stressors span levels without one primary level. CAVE: may be auto-calculated during data extraction, but check manually | mandatory | string | nominal | input field | vs_stressor_level | NA | NA | NA | societal | active | ResiMETA_Coding_Manual_Stressor_Identification_and_Categories | |
| stressor_chronicity | no | lev1_report_samples | index stressor | Temporal persistence/type of the index stressor. | Apply the project chronicity definitions. | mandatory | string | nominal | drop-down list (single) | vs_stressor_chronicity | NA | NA | NA | transitional_normative | active | ResiMETA_Coding_Manual_Stressor_Chronicity | |
| stressor_collective_event_date | no | lev1_report_samples | index stressor | Exposure-onset date for a collectively experienced acute societal event with uniform onset across the analytic sample. | Use only for acute collective events with an identical onset for the entire analytic sample (e.g., natural disaster date; terrorist attack date). Record exact YYYY-MM-DD; when not reported, an official event date may be used. Do not use for prolonged, role-based. or individually varying exposure (e.g., disaster volunteers, health care workers during a pandemic, extended deployments). | context-dependent | date | continuous | input field | YYYY-MM-DD | NA | NA | NA | 2016-06-23T0:0:0+0000 | active | NA | |
| stressor_comments | outdated version | lev1_report_samples | index stressor | Free-text notes on index-stressor coding decisions. | Use sparingly for ambiguity, special cases, or deviations from standard stressor rules. | context-dependent | string | nominal | input field | NA | NA | NA | NA | exposure date based on official event date | active | NA | |
| sample_pre_stressor_data | outdated version | availability and type of pre-stressor outcome data relative to the index stressor across all modeled outcomes in the study | # reference identified index stressor # prospective: ≥1 modeled timepoint clearly before index stressor exposure # retrospective: no prospective modeled timepoint, but but pre-stressor state is assessed retrospectively (e.g., "recall XY before the event", "remember pre-pandemic state"); check instrument wording and assessment description carefully - recall frame must clearly refer to pre-stressor period # none: all modeled timepoints occur at or after stressor exposure # unclear: insufficient information to determine timing relative to stressor exposure | mandatory | string | nominal | drop-down list (single) | {prospective, restrospective, none, unclear} | NA | NA | {lev2_trajectories.traj_pre_stressor_data} | prospective | NA | ||||
| traj_model_id | no | lev2_trajectory_models | identity | Stable identifier for one trajectory analysis/model solution. | Format: tmod_####_## ####: zero-padded report_num ##: sequential trajectory-model number within the report (e.g., _01, _02), regardless of sample, outcome, or model type Use _01 for a single-trajectory-model report. Once assigned, do not renumber. | mandatory | string | nominal | input field | NA | NA | NA | NA | tmod_0438_01 | active | NA | |
| traj_model_sample_overlap_primary | no | no | lev2_trajectory_models | selection | Indicates whether this trajectory-model record is retained for main trajectory analyses after resolving cross-report sample dependency. | TRUE = retained or no cross-report sample-overlap selection is applicable. FALSE = excluded because an overlapping report/model is preferred. | mandatory | boolean | binary | drop-down list (single) | vs_boolean | NA | NA | NA | true | active | ResiMETA_Sample_Overlaps |
| traj_model_within_report_sample_outc_primary | no | no | lev2_trajectory_models | selection | Primary-selection flag indicating whether the trajectory-model/outcome record is retained as the preferred representation of its harmonized outcome construct among competing outcomes modeled within the same report_sample_id. | CATEGORIES # TRUE: selected outcome (preferred representation OR no competing outcome within the same report_sample_id) # FALSE: another competing outcome within the same report_sample_id provides the preferred representation SCOPE # apply only among competing outcomes within the same report_sample_id # outcomes are considered competing when at least one of the following applies: ## they are mapped to the same harmonized outcome construct ## they represent overlapping facets, domains, subscales, or total/composite scores of the same instrument and one provides a clear broader, more appropriate, or conceptually preferable representation ## there is an explicit broader-narrower or component-composite relationship such that one outcome substantially encompasses the other, even if the outcomes are mapped to different harmonized constructs # do not treat outcomes as competing solely because they are correlated, belong to the same broad psychopathology/well-being domain, or represent related but distinct constructs # use of different instruments does not by itself establish or exclude competition: outcomes measured with different instruments compete when they are mapped to the same harmonized construct, but related outcomes mapped to different constructs generally remain separate unless an explicit hierarchical/component relationship exists # if no competing outcome exists within the report_sample_id, code TRUE # RF retention is decided independently at RF level SELECTION RULES (apply in order) 1. prefer a suitable total or composite score over separately modeled subscales or facets when it adequately represents the harmonized outcome construct # e.g., SDQ externalizing composite over the conduct-problem or hyperactivity subscale 2. if no suitable total/composite is available, prefer the broader outcome representation when it clearly encompasses the narrower competing outcome without substantially changing the intended construct # e.g., YSR Internalizing Problems over YSR Anxious/Depressed when both are treated as competing representations of internalizing distress # do not apply this rule merely because one distinct construct is conceptually broader than another 3. if several competing facets/subscales remain, prefer the outcome with the closest conceptual match to {outc_label}, greater construct specificity, and less overlap with other constructs; examples: # mixed_positive_mental_health: WHOQOL psychological health over social relationships; emotional well-being over social and family well-being # PTSD_symptoms: re-experiencing over hyperarousal, dysphoric arousal, or numbing # externalizing_problems: conduct problems over hyperactivity 4. among otherwise defensible alternatives, prefer the outcome that is more consistently harmonizable across ResiMETA studies 5. if construct fit and coverage are comparable, consider author-designated priority (e.g. primary or central outcome) 6. use measurement quality as a final criterion, considering validation, construct-relevant item coverage, reliability, consistency across timepoints, and use of a full scale rather than isolated items CAVE # do not use differences in trajectory-model specification alone for this decision # do not select one outcome over another merely because it is more general, more severe, or belongs to a broader conceptual domain # if unresolved after applying the hierarchy, discuss with senior team members and document in {outc_comments} | mandatory | boolean | binary | drop-down list (single) | vs_boolean | NA | NA | NA | true | active | NA |
| traj_model_nos_score | no | lev2_trajectory_models | quality | Methodological quality score for the trajectory-model/outcome record using the modified Newcastle-Ottawa Scale. | Use the model/outcome-level rating where available. | context-dependent | float | continuous | input field | [0.00, 100.00] | percent | 2 | NA | 66.67 | active | NOS Table | |
| outc_name | yes | lev2_trajectory_models | outcome | Outcome name as stated by the report authors. | Record the authors’ wording for the modeled construct, not the assessment tool. Retain construct-defining qualifiers (e.g., "state anxiety", "intrusion symptoms"); remove sample/timing descriptors (e.g., "maternal depressive symptoms" -> "depressive symptoms"; "antenatal depressive symptoms" -> "depressive symptoms"; "cancer-specific anxiety" -> anxiety). For merged outcomes use the authors' composite label. | mandatory | string | nominal | input field | NA | NA | NA | NA | depressive symptoms | active | ResiMETA Screening Rules | |
| outc_merged | yes | lev2_trajectory_models | outcome | Indicates whether the modeled trajectory combines more than one assessment tool or construct. | TRUE if the trajectory model is based on: # multiple separately assessed constructs or scores combined into one modeled outcome. CAVE: Do not code TRUE solely because a validated total score combines multiple subscales or domains within a single instrument (e.g., YSR Total Problems, SDQ Total Difficulties); code TRUE only when distinct constructs are combined ad hoc, either across different tools or across subscales/domains of the same instrument without an established validated composite score. # multiple tools combined assessing the same construct (e.g., two PTSD scales) # tool changes across modeled timepoints while assessing the same construct (e.g., scale A at baseline, scale B at follow-up) FALSE if the trajectory model is based on one assessment tool, including validated total/composite scores that combine subscales or domains within that instrument, and individual subscales or closely related versions of the same instrument unless they differ substantially in item content or construct definition. | mandatory | boolean | binary | drop-down list (single) | vs_boolean | NA | NA | NA | false | active | NA | |
| outc_merged_type | no | lev2_trajectory_models | outcome | Type of outcome merging used in the trajectory model. | Code one category only when outc_merged = TRUE. | conditional: outc_merged = TRUE | string | nominal | drop-down list (single) | vs_outc_merged_type | NA | NA | NA | multi_construct_composite | active | NA | |
| outc_tool_origin | no | lev2_trajectory_models | outcome | Provenance of the assessment basis underlying the modeled outcome. | Classify the origin of the assessment basis, regardless of validation status. | mandatory | string | nominal | drop-down list (single) | vs_outc_tool_origin | NA | NA | NA | standardized_instrument | active | NA | |
| outc_tool_name | yes | lev2_trajectory_models | outcome | full assessment tool name(s) as reported by authors | # record the full tool name(s) as reported by the authors # if {outc_merged} = TRUE: list all tools separated by ", " # if {outc_merged_type} = "tool switch over time": list tools in chronological order and add modeled timepoints in braces, e.g., Tool 1 (T1), Tool 2 (T2, T3); refer only to timepoints included in the trajectory model and standardize the modeled waves as T1, T2, T3, ... with T1 as the earliest modeled assessment # if self-constructed questions are used, extract the item or question wording verbatim without quotation marks | mandatory | string | nominal | input field | NA | NA | NA | NA | Edinburgh Postnatal Depression Scale | NA | ||
| outc_tool_abbr | yes | lev2_trajectory_models | outcome | assessment tool abbreviation(s) as reported by authors | # extract the abbreviation(s) as reported by the authors # if {outc_merged} = TRUE: list all abbreviations separated by ", " # if {outc_merged_type} = "tool switch over time": keep the same chronological order as {outc_tool_name} # if no abbreviation is reported or only self-constructed questions are used: code NA | context-dependent | string | nominal | input field | NA | NA | NA | NA | EPDS | NA | ||
| outc_tool_label | no | lev2_trajectory_models | outcome | standardized assessment tool name(s) according to the ResiMETA assessment instrument dictionary | # map each tool in {outc_tool_name} to the standardized tool label in the ResiMETA assessment instrument dictionary # use the citation and study description to identify the exact instrument, as author naming is sometimes ambigous # if {outc_merged} = TRUE: list all standardized tool names separated by ", " # if mapping is not yet possible: code "not yet mapped" # if no standardized instrument was used: code NA | mandatory | string | nominal | drop-down list (multiple) | NA | NA | NA | Edinburgh Postnatal Depression Scale | Instrument Dictionary | |||
| outc_tool_id | lev2_trajectory_models | outcome | unique assessment tool identifier(s) according to the ResiMETA assessment instrument dictionary | NA | mandatory | string | nominal | input field | NA | NA | NA | {outc_tool_label} | epds | Instrument Dictionary | |||
| outc_tool_version | no | lev2_trajectory_models | outcome | version or specifier information for the assessment tool(s) | # record only version and specifier information that meaningfully affects interpretation of the measured construct or score composition ## short form or item subset ## subscales or domain-specific scores ## single-item specifications ## developmental or population-specific adaptations (e.g., child version) ## interpretatively relevant content modifications (e.g., exclusion or replacement of specific items) # standardize wording according to the ResiMETA assessment instrument dictionary where possible # preferred format examples: "10-item version", "revised version", "anxiety subscale", "single item", "child version", "without suicidality item", "5 items" # use wording such as "X-item version" only for established or explicitly described short forms documented in the ResiMETA instrument dictionary or clearly presented as a named short version by the study authors # if items were selectively removed, replaced, or modified in a way that changes construct interpretation, describe the content modification (e.g., "without suicidality item") if described by authors # if the instrument was reduced to a subset of items but the removed content is unclear, unspecified, or not part of an established short form, record only the retained item count (e.g., "5 items") # implied subscale specification may be added for clarity when the modeled outcome clearly reflects a specific subscale, even if not explicitly stated by the authors # do not include ## language or translation descriptors ## administration details that do not meaningfully alter interpretation ## response format or rating-scale modifications unless they represent a substantively distinct measurement version ## scoring procedures or psychometric information ## "total score" if the full instrument was used ## event-specific contextualization of otherwise unchanged items or instructions (e.g., asking respondents to answer with reference to the focal disaster) # if {outc_merged} = TRUE and different versions or specifiers apply: list them for each {outc_tool_id} separated by ", " (e.g., "caps - 3-item version, hads - anxiety subscale") | context-dependent | string | nominal | input field | NA | NA | NA | NA | anxiety subscale | Instrument Dictionary | ||
| outc_assessment_modality | outdated version | lev2_trajectory_models | outcome | informant source of the outcome rating# | # code one category ## self-report = participant directly reports on own symptoms or state ## informant-report = another person rates the participant (e.g., parent, teacher, partner) ## clinician- or interviewer-rated = rating based on clinician assessment or structured interview ## registry or administrative = outcome derived from records, registry data, or administrative sources without direct symptom rating ## mixed = trajectory model combines more than one informant type # NA: insufficient information to determine assessment modality # default-modality rule: if modality is not explicitly stated, use the tool’s default modality from the ResiMETA assessment instrument dictionary ONLY if explicitly defined in dictionary AND the study design and sample make alternatives implausible; otherwise code NA and document the ambiguity in {outc_comments} | mandatory | string | nominal | drop-down list (single) | {self_report, informant_report, clinician_or_interviewer_rated, registry_or_administrative, mixed, unclear} | NA | NA | NA | self_report | NA | ||
| outc_label | outdated version | lev2_trajectory_models | outcome | standardized outcome label according to the ResiMETA construct dictionary | # map each outcome in {outc_name} to the most specific applicable standardized outcome label in the ResiMETA construct dictionary # classify the specific score used in the trajectory analysis ## if a subscale, domain, facet, or item set is modeled: map to the construct represented by that specific score rather than the full instrument ## if multiple scores from the same instrument are modeled separately: assign a separate construct mapping to each score ## do not split a validated total or composite score into assumed components that were not separately modeled # identify the construct primarily from the standardized and validated definition of the exact modeled score ({outc_tool_label}, {outc_tool_version}, subscale/domain) ## prioritize the construct label, score definition, and interpretation provided by the instrument developers over an independent reviewer interpretation of individual items ## use item content as secondary evidence to identify and characterize potential discrepancies, but do not relabel a standardized and validated score solely because its items are predominantly positively or negatively oriented ## if the validated score interpretation and the predominant item content are not fully consistent: retain the validated construct mapping and document the discrepancy in {outc_comments}; flag the outcome for sensitivity analysis where applicable ## preserve the original instrument and subscale name, validated score interpretation, and scoring direction # classify the construct being measured, irrespective of scoring direction ## reverse-coded symptom scores remain mapped to the applicable {construct_label} within {construct_category} = "mental_distress" ## reverse-coded impairment scores remain mapped to the applicable {construct_label} within {construct_category} = "psychosocial_functioning_and_impairment" ## absence of mental distress or impairment does not by itself constitute positive mental health ## do not numerically invert a validated score solely because its harmonized construct category differs from its scoring direction # prefer the most specific defensible {construct_label} over a broader or mixed construct label ## use a broader or mixed label only if the modeled score intentionally integrates multiple constructs and no more specific construct adequately represents the validated score ## use "psychological_distress" when the modeled score explicitly assesses nonspecific or general psychological distress ## use "mixed_positive_mental_health" when the validated score represents an integrative, multidimensional, global, or otherwise broadly defined positive mental health, general mental health, or mental/psychosocial quality-of-life construct that cannot be assigned to a more specific positive mental health label ## broad or mixed labels are not uncertainty categories; if the construct itself cannot be established, code "not yet mapped" # merged tools: ## if {outc_merged_type} = "multi tool same construct": map to the shared construct ## if {outc_merged_type} = "multi construct composite": map to the validated broader or mixed construct represented by the composite (e.g., "psychological_distress", "internalizing_symptoms", "mixed_positive_mental_health") ## if a multi-construct composite spans higher-order outcome categories and has no validated higher-order interpretation: code "not yet mapped" # for modified, nonstandard, or author-constructed items or scales: classify primarily according to the actual item content, response anchors, scoring, and authors’ intended interpretation ## verify the authors’ labeling and adjust the mapping if it is semantically inconsistent with the score content ## do not prefer an author-constructed multi-item composite over a single item based on item count alone # if mapping is not possible: code "not yet mapped" | mandatory | string | nominal | drop-down list (single) | {psychological_distress, depressive_symptoms, anxiety_symptoms, PTSD_symptoms, stress_symptoms, positive_mental_health, loneliness, internalizing_symptoms, externalizing_symptoms, negative_affect, positive_affect, eudiamonic_wellbeing, hedonic_wellbeing, suicidality, grief_symptoms, rumination, resilience, functional_impairment, interpersonal_problems, family_functioning, sleep_quality, not_yet_mapped} | NA | NA | NA | depressive_symptoms | Construct Dictionary | ||
| outc_label_id | no | lev2_trajectory_models | unique outcome identifier according to the ResiMETA construct dictionary | NA | mandatory | string | nominal | drop-down list (single) | {dis, dep, anx, ptsd, str, pmh, hwb, ewb, lon, int, ext, naf, paf, sui, gri, rum, res, imp, ipr, ffn, slp} | NA | NA | {outc_label} | dep | Construct Dictionary | |||
| outc_category | outdated version | lev2_trajectory_models | higher-order outcome category according to the ResiMETA construct dictionary | NA | mandatory | string | nominal | drop-down list (single) | {mental_distress, positive_mental_health, resilience, psychosocial_functioning_and_impairment} | NA | NA | {outc_label} | mental_distress | Construct Dictionary | |||
| outc_comments | outdated version | lev2_trajectory_models | free-text notes on outcome coding decisions | # use to document special cases, additional information and data sources, and deviations from standard extraction rules # avoid duplicating structured fields | context-dependent | string | nominal | input field | NA | NA | NA | NA | anxiety subscale is one component of internalizing problems subscale | NA | |||
| Level 2.2: Study Design | lev2_trajectory_models | ||||||||||||||||
| traj_pre_stressor_data | yes | lev2_trajectory_models | classification of the availability and type of pre-stressor data in the trajectory model, relative to the index stressor | # reference identified index stressor {stressor_label} # prospective: ≥1 modeled timepoint clearly before index stressor exposure # retrospective: no prospective modeled timepoint, but pre-stressor state is assessed retrospectively (e.g., "recall XY before the event", "remember pre-pandemic state"); check instrument wording and assessment description carefully - the instrument or question must explicitly elicit recall of the participant's own pre-stressor state, not general pre-event norms or population comparisons; the recall frame must clearly refer to the pre-stressor period # none: all modeled timepoints occur at or after stressor exposure # unclear: insufficient information to determine timing relative to stressor exposure; treat as none for all downstream variable coding (conservative default) and document in {traj_tp_comments} | mandatory | string | nominal | drop-down list (single) | {prospective, restrospective, none, unclear} | NA | NA | NA | prospective | NA | |||
| traj_tp_total | yes | lev2_trajectory_models | total number of measurement timepoints included in the trajectory model | # count only timepoints included in the trajectory model; see supporting manual # must equal {traj_tp_pre} + {traj_tp_post} # if {traj_pre_stressor_data} ∈ {none, unclear}: {traj_tp_total} = {traj_tp_post} | mandatory | integer | discrete | input field | [3, ∞] | NA | NA | NA | 5 | Coding Manual Timepoints | |||
| traj_tp_pre | yes | lev2_trajectory_models | number of measurement timepoints before index stressor exposure | # count modeled timepoints strictly before index stressor exposure; see supporting manual # classify ambiguous waves (e.g., timepoints including both exposed and unexposed individuals) using study-level temporal anchoring rules; if study-level anchoring is insufficient to resolve the classification, classify the wave as post-stressor (conservative default) and document in {traj_tp_comments} # count retrospective assessments # if {traj_pre_stressor_data} ∈ {none, unclear}: code 0 | mandatory | integer | discrete | input field | [0, ∞] | NA | NA | NA | 2 | Coding Manual Timepoints | |||
| traj_tp_post | yes | lev2_trajectory_models | number of measurement timepoints at or after index stressor exposure | # count modeled timepoints at or after index stressor exposure; see supporting manual # if {traj_pre_stressor_data} ∈ {none, unclear}: {traj_tp_total} = {traj_tp_post} | mandatory | integer | discrete | input field | [0, ∞] | NA | NA | NA | 3 | Coding Manual Timepoints | |||
| traj_tp_first_date | no | lev2_trajectory_models | calendar date of the first assessment wave used in the trajectory model | # code the first time point at which the trajectory outcome was assessed and included in the trajectory model # use the highest precision reported in the study: YYYY-MM-DD if the exact date is available, YYYY-MM if only month and year are available, and YYYY if only the year is available # do no mt impute missing month or day values by default, e.g., do not code 2020-01-01 when only 2020 is reported # if the first assessment wave is reported as a calendar interval or data collection window, code the start of that period, e.g., March-May 2020 -> 2020-03 # if assessment timing differs between participants, code the earliest reported or inferable calendar date or period at which the first modeled assessment occurred # if only a subset of study waves is included in the trajectory model, code the first wave actually used in that model # for uniquely identifiable collective events, such as a named earthquake, terrorist attack, natural disaster, or pandemic-related event, the event date may be verified using {stressor_collective_event_date} or a reliable external source when this is necessary to determine the calendar timing of an assessment. Retain only the precision justified by the reported assessment timing. Example: T1 occurred 6 months after a clearly identified earthquake reported only as occurring in 2008 -> verify the earthquake date to determine whether T1 occurred in 2008 or 2009 # an assessment date may be inferred when ## an explicit anchor date or interval is reported for the analyzed participants, e.g., exposure date, birth-date range, expected-delivery range, surgery date, or diagnosis date; and ## the timing of the modeled assessment relative to that anchor is clearly reported, e.g., 6 months after exposure, at age 3 months, or at 32 weeks of gestation ## use the earliest justified anchor date combined with the relative timing of the first modeled assessment ## do not infer dates from recruitment, eligibility, intervention, or overall study periods unless they clearly determine the timing of the modeled trajectory-outcome assessment ## do not infersment date by calculating through an intermediate event whose calendar d an assesate is not reported or determinable. A timing limit or range relative to that event does not establish the event’s actual date and must not be treated as an exact participant timing. Example: recruited May 2008-December 2010 before 25 weeks’ gestation; T4 at 12 months postpartum -> code NA unless the actual or expected birth-date range is reported ## retain only the justified precision; nominal timings such as “6 months after” should generally be coded to month precision, even if the anchor includes an exact day ## examples: ### tornado on 2016-06-23; T1 6 months after -> 2016-12 ### expected-delivery range 1991-04-01 to 1992-12-31; T1 at 32 weeks of gestation -> 1991-02 ### birth-date range 2004-01-01 to 2004-12-31; T1 at age 3 months -> 2004-04 # code NA if no calendar date or period is reported or can be inferred with sufficient confidence # CAVE: this variable refers to trajectory-outcome assessments used in the trajectory model, not necessarily to recruitment, the overall study period, intervention periods, or predictor assessments | mandatory | string | continuous | input field | NA | NA | NA | NA | 2020-01-10T0:0:0+0000 | NA | |||
| traj_tp_last_date | no | lev2_trajectory_models | calendar date of the last assessment wave used in the trajectory model | # code the last time point at which the trajectory outcome was assessed and included in the trajectory model # use the highest precision reported in the study: YYYY-MM-DD if the exact date is available, YYYY-MM if only month and year are available, and YYYY if only the year is available # do not impute missing month or day values by default, e.g., do not code 2020-12-31 when only 2020 is reported or 2020-05-31 when only 2020-05 is reported # if the last assessment wave is reported as a calendar interval or data collection window, code the end of that period, e.g., March-May 2020 -> 2020-05 # if assessment timing differs between participants, code the latest reported or inferable calendar date or period at which the last modeled assessment occurred # if only a subset of study waves is included in the trajectory model, code the last wave actually used in that model # for uniquely identifiable collective events, such as a named earthquake, terrorist attack, natural disaster, or pandemic-related event, the event date may be verified using {stressor_collective_event_date} or a reliable external source when this is necessary to determine the calendar timing of an assessment. Retain only the precision justified by the reported assessment timing. Example: T1 occurred 6 months after a clearly identified earthquake reported only as occurring in 2008 -> verify the earthquake date to determine whether T1 occurred in 2008 or 2009 # an assessment date may be inferred when: ## an explicit anchor date or interval is reported for the analyzed participants, e.g., exposure date, birth-date range, expected-delivery range, surgery date, or diagnosis date; and ## the timing of the modeled assessment relative to that anchor is clearly reported, e.g., 6 months after exposure, at age 3 months, or at 32 weeks of gestation ## use the latest justified anchor date combined with the relative timing of the last modeled assessment ## do not infer dates from recruitment, eligibility, intervention, or overall study periods unless they clearly determine the timing of the modeled trajectory-outcome assessment ## do not infer an assessment date by calculating through an intermediate event whose calendar date is not reported or determinable. A timing limit or range relative to that event does not establish the event’s actual date and must not be treated as an exact participant timing. Example: recruited May 2008-December 2010 before 25 weeks’ gestation; T4 at 12 months postpartum -> code NA unless the actual or expected birth-date range is reported ## retain only the justified precision; nominal timings such as “6 months after” should generally be coded to month precision, even if the anchor includes an exact day ## examples: ### tornado on 2016-06-23; last modeled assessment 6 months after -> 2016-12 ### expected-delivery range 1991-04-01 to 1992-12-31; last modeled assessment at 32 weeks of gestation -> 1992-11 ### birth-date range 2004-01-01 to 2004-12-31; last modeled assessment at age 3 months -> 2005-03 # code NA if no calendar date or period is reported or can be inferred with sufficient confidence # CAVE: this variable refers to trajectory-outcome assessments used in the trajectory model, not necessarily to recruitment, the overall study period, intervention periods, or predictor assessments | mandatory | string | continuous | input field | NA | NA | NA | NA | 2020-07-01T0:0:0+0000 | NA | |||
| traj_tp_desc | yes | lev2_trajectory_models | description of timing of all modeled measurement timepoints relative to the index stressor | # include all timepoints used in modeling; count must match {traj_tp_total} # format: "T1: …, T2: …, T3: …" # label timepoints sequentially (T1…Tn), independent of authors’ original wave naming # describe each timepoint relative to the ResiMETA-defined index stressor # each timepoint description must be independently interpretable; do not rely on information from other timepoints to understand timing # preserve original time units and key wording from the study (e.g., months, weeks, years) # harmonize phrasing across similar stressors where possible, without altering meaning # include contextual qualifiers in parentheses where useful (e.g., months, calendar year) # clearly label retrospective baseline assessments (e.g., "T1: retrospective baseline (pre-stressor recall)") # see supporting manual for special cases (timing to index stressor cannot be determined, cohort data, perinatal stressors) | mandatory | string | nominal | input field | NA | NA | NA | NA | T1: at 17.4 +/− 4.9 weeks of gestation, T2: at 30.6 +/− 2.7 weeks of gestation and T3: at 4.2 +/−2.1 weeks postpartum | Coding Manual Timepoints | |||
| traj_time_last_pre_to_stressor_onset | no | lev2_trajectory_models | time interval between last pre-stressor timepoint and onset of index stressor | # applicable only if {traj_pre_stressor_data} = prospective # if {traj_pre_stressor_data} = {restrospective, none, unclear}: code NA # preserve author wording and units # express interval relative to the index stressor # if timing is reported as a range, use the midpoint per supporting manual rules # if timing is not reported clearly enough to determine the interval, estimate based on available information and document approximation (e.g., approx. 1 year); otherwise code NA | conditional: {traj_pre_stressor_data} = prospective | string | nominal | input field | NA | NA | NA | NA | approx. 8 weeks | Coding Manual Timepoints | |||
| traj_time_last_pre_to_stressor_onset_weeks | no | lev2_trajectory_models | time interval in weeks between last pre-stressor timepoint and onset of index stressor | # derived from {traj_time_last_pre_to_stressor_onset} # if {traj_pre_stressor_data} ∈ {retrospective, none, unclear}: code NA # if {traj_time_last_pre_to_stressor_onset} = NA: code NA # convert to weeks using supporting manual rules # if {traj_time_last_pre_to_stressor_onset} is an approximation, convert the numeric portion | conditional: {traj_pre_stressor_data} = prospective | float | continuous | input field | [0, ∞] | weeks | 2 | NA | 8 | Coding Manual Timepoints | |||
| traj_time_stressor_onset_to_first_post | no | lev2_trajectory_models | time interval from onset of the ResiMETA-defined index stressor to the first modeled timepoint at or after stressor onset | # identify the first modeled post-stressor timepoint (see {traj_tp_post}); post-stressor refers to timing at or after stressor onset and does not imply that stressor exposure has ended # extract the elapsed time from stressor onset to this timepoint, irrespective of whether stressor exposure is still ongoing # preserve author wording and units where possible # apply range, approximation, cohort, and stressor-specific rules according to the supporting manual # if timing cannot be defensibly anchored to stressor onset, code NA | mandatory | string | nominal | input field | NA | NA | NA | NA | 4.2 weeks | Coding Manual Timepoints | |||
| traj_time_stressor_onset_to_first_post_weeks | no | lev2_trajectory_models | time interval in weeks from onset of the ResiMETA-defined index stressor to the first modeled timepoint at or after stressor onset | # convert {traj_time_stressor_onset_to_first_post} to weeks according to the supporting manual # use standard conversion constants and round to 2 decimal places # code 0 only if the actual timing interval is zero; ongoing stressor exposure does not imply 0 weeks # if {traj_time_stressor_onset_to_first_post} cannot be quantified, code NA | mandatory | float | continuous | input field | [0, ∞] | weeks | 2 | NA | 4.2 | Coding Manual Timepoints | |||
| traj_first_post_exposure_status | no | lev2_trajectory_models | status of the ResiMETA-defined index stressor exposure at the first modeled timepoint at or after stressor onset | # assess exposure status at the same modeled timepoint used for {traj_time_stressor_onset_to_first_post} # base coding on the specific index stressor exposure defined in ResiMETA, not on persistence of symptoms, adaptation, grief, or secondary consequences # code during_stressor_exposure if the relevant exposure is ongoing at the timepoint # code after_stressor_exposure if the relevant exposure has ended by the timepoint # code heterogeneous if participants are known to differ in exposure status at the timepoint # code unclear if available information is insufficient to determine exposure status # do not infer exposure status solely from the stressor being classified as acute, chronic, or transitional # see supporting manual for detailed rules and stressor-specific examples | mandatory | string | nominal | drop-down list (single) | {during_stressor_exposure, after_stressor_exposure, heterogeneous, unclear} | NA | NA | NA | after_stressor_exposure | Coding Manual Timepoints | |||
| traj_time_stressor_onset_to_last_post | no | lev2_trajectory_models | time interval from onset of the ResiMETA-defined index stressor to the last modeled timepoint at or after stressor onset | # identify the last modeled post-stressor timepoint (see {traj_tp_post}); post-stressor refers to timing at or after stressor onset and does not imply that stressor exposure has ended # extract the elapsed time from stressor onset to this timepoint, irrespective of whether stressor exposure is still ongoing # preserve author wording and units where possible # apply range, approximation, cohort, and stressor-specific rules according to the supporting manual # if timing cannot be defensibly anchored to stressor onset, code NA | mandatory | string | nominal | input field | NA | NA | NA | NA | 73 months | Coding Manual Timepoints | |||
| traj_time_stressor_onset_to_last_post_weeks | no | lev2_trajectory_models | time interval in weeks from onset of the ResiMETA-defined index stressor to the last modeled timepoint at or after stressor onset | # convert {traj_time_stressor_onset_to_last_post} to weeks according to the supporting manual # use standard conversion constants and round to 2 decimal places # code 0 only if the actual timing interval is zero; ongoing stressor exposure does not imply 0 weeks # if {traj_time_stressor_onset_to_last_post} cannot be quantified, code NA | mandatory | float | continuous | input field | [0, ∞] | weeks | 2 | NA | 317.42 | Coding Manual Timepoints | |||
| traj_time_stressor_end_to_last_post | no | lev2_trajectory_models | time interval from the end of the ResiMETA-defined index stressor exposure to the last modeled timepoint occurring after stressor exposure has ended | # identify the last modeled post-stressor timepoint (see {traj_tp_post}) # if stressor exposure has ended by this timepoint, extract the elapsed time from stressor end to this timepoint # preserve author wording and units where possible # apply range, approximation, cohort, and stressor-specific rules according to the supporting manual # if stressor exposure is still ongoing at the last modeled timepoint, or timing cannot be defensibly anchored to stressor end, code NA # for discrete acute stressors, this variable equals {traj_time_stressor_onset_to_last_post} | mandatory | float | continuous | input field | [0, ∞] | weeks | 2 | NA | 317.42 | Coding Manual Timepoints | |||
| traj_time_stressor_end_to_last_post_weeks | no | lev2_trajectory_models | time interval in weeks from the end of the ResiMETA-defined index stressor exposure to the last modeled timepoint occurring after stressor exposure has ended | # convert {traj_time_stressor_end_to_last_post} to weeks according to the supporting manual # use standard conversion constants and round to 2 decimal places # code 0 only if the actual timing interval is zero; ongoing stressor exposure does not imply 0 weeks # if {traj_time_stressor_end_to_last_post} cannot be quantified, code NA | mandatory | float | continuous | input field | [0, ∞] | weeks | 2 | NA | 317.42 | Coding Manual Timepoints | |||
| traj_last_post_exposure_status | no | lev2_trajectory_models | status of the ResiMETA-defined index stressor exposure at the last modeled timepoint at or after stressor onset | # assess exposure status at the same modeled timepoint used for {traj_time_stressor_onset_to_last_post} # base coding on the specific index stressor exposure defined in ResiMETA, not on persistence of symptoms, adaptation, grief, or secondary consequences # code during_stressor_exposure if the relevant exposure is ongoing at the timepoint # code after_stressor_exposure if the relevant exposure has ended by the timepoint # code heterogeneous if participants are known to differ in exposure status at the timepoint # code unclear if available information is insufficient to determine exposure status # do not infer exposure status solely from the stressor being classified as acute, chronic, or transitional # see supporting manual for detailed rules and stressor-specific examples | mandatory | string | nominal | drop-down list (single) | {during_stressor_exposure, after_stressor_exposure, heterogeneous, unclear} | NA | NA | NA | after_stressor_exposure | Coding Manual Timepoints | |||
| traj_n_by_tp | no | lev2_trajectory_models | sample size at each modeled measurement timepoint | EXTRACTION # extract sample size (n) for each measurement timepoint included in the trajectory model # ensure consistency with {traj_tp_total} and {traj_tp_desc} # format: "T1: n1 , T2: n2, T3: n3, …" - label timepoints sequentially (T1…Tn), independent of authors’ naming # if outcome-specific or instrument-specific n is reported, prioritize it over overall sample n for modeled timepoints # if information is reported in a referenced publication, retrieve from that source as needed # if n is unavailable for some timepoints, report available values only; do not impute missing values BASELINE IDENTIFICATION # baseline = participants meeting study eligibility criteria at study begin. Eligibility refers to participant characteristics or exposure status, not data-completeness requirements (e.g., availability of outcome data, minimum number of observed waves, complete covariate data) # if authors distinguish enrolled from included participants, and some enrolled persons never participated, did not consent, or were not actual study participants: use the included count as baseline # do not treat an "eligible for analysis", "analytic sample", or "trajectory-modeling sample" as baseline when its definition rests on data-completeness restrictions BASELINE LABELING # label as "baseline (T1)" if the valid baseline corresponds to the first modeled timepoint # label as "baseline (T0)" if the valid baseline refers to an earlier, non-modeled timepoint # if authors label a timepoint as "baseline", apply this label only when it is consistent with the ResiMETA baseline definition above; i.e., if the author-labeled baseline refers only to the analytic or modeling sample after data-completeness restrictions, do not mark it as baseline SPECIAL CASES # if baseline n and analytic/modeling n are available but timepoint-specific n values are not: ## code {traj_n_by_tp} = NA ## record the available information in {traj_attrition_info} # if no usable information is available: code NA | mandatory | string | nominal | input field | NA | NA | NA | NA | baseline (T0): 997, T1: 951, T2: 977, T3: 856 | NA | |||
| traj_attrition_info | no | lev2_trajectory_models | supplementary attrition information not captured in structured variables | # record concise free-text information needed to interpret {traj_attrition_rate} or to explain why a valid attrition calculation is not possible # include only information not already captured in structured variables. Relevant content includes: special calculation circumstances, partial follow-up, merged cohorts, retention or response rates pertinent to interpretation, and the n of all participants meeting inclusion criteria without n per tiempeoint # do not include: full sample-size-by-timepoint listings, final analytic sample descriptions, general eligibility or exclusion rules, or detailed dropout predictor analyses # summarize in factual, concise language; do not quote verbatim from source text # if no relevant information is available: code NA | context-dependent | string | nominal | input field | NA | NA | NA | NA | 2 cohorts mixed togehter from T2 // attrition is calculated based on the data for each cohort individually (see Figure 1) // cohort 1: 11.36, cohort 2: 9.6 | NA | |||
| traj_attrition_rate | no | lev2_trajectory_models | maximum attrition rate (%) across modeled measurement timepoints | DEFINITIONS # attrition = loss of participants from the study baseline sample to the trajectory analysis sample, or across modeled measurement timepoints. Purpose: to indicate how representative the analytic trajectory sample is relative to eligible study participants at baseline # express attrition as a percentage (0–100), rounded to 2 decimal places # baseline = participants meeting study eligibility criteria at study begin. Eligibility refers to participant characteristics or exposure status, not data-completeness requirements (e.g., minimum observed waves, complete covariate data). If authors distinguish enrolled from included participants, and some enrolled persons never participated, did not consent, or were not actual study participants: use the included count as baseline. PREFERRED CALCULATION (priority order, descending): 1. use an author-reported attrition rate if it is clearly defined, interpretable, and consistent with the ResiMETA baseline definition. 2. if timepoint-specific sample sizes are available, calculate attrition as the percentage reduction from baseline n to the lowest observed modeled timepoint n; record timepoint-specific values in {traj_n_by_tp} 3. if baseline n and analytic/modeling n are available but timepoint-specific n values are not: calculate attrition from baseline n to analytic/modeling n # code {traj_n_by_tp} = NA # document the basis in {traj_attrition_info} # code {traj_attrition_basis} = partial information DO NOT CALCULATE ATTRITION IF # only the analytic or modeling n is reported and no baseline n is available. # the only available "baseline" is defined by data-completeness criteria (e.g., participants with ≥2 outcome assessments, ≥3 waves, complete covariate data, or analytic subsamples restricted by missing longitudinal outcome data) SPECIAL CASES # staggered-entry designs: Iignore reduced n at timepoints that occur before participants entered observation, base attrition on baseline-to-analysis loss or subsequent dropout # partial information: calculate only if a valid baseline n and at least one later reduced n are available ## code {traj_attrition_basis} = partial information ## document available information in {traj_attrition_info} # merged subsamples: if attrition is reported separately for subsamples that are combined in the trajectory model, extract the higher value for conservative coding and record all subsample-specific values in {traj_attrition_info} # if no usable baseline or attrition information is available: code NA | mandatory | float | continuous | input field | [0, 100] | percent | variable | NA | 47.68 | NA | |||
| traj_attrition_basis | no | lev2_trajectory_models | data basis used to derive the attrition rate | # classify the source or calculation basis of {traj_attrition_rate} # author-reported: attrition rate stated directly by the authors and clearly interpretable without re-calculation # calculated (baseline reference): attrition calculated from a valid baseline timepoint n to a later modeled timepoint n # calculated (non-baseline reference): attrition calculated relative to a later timepoint because a valid baseline n is unavailable # partial information: an attrition rate is available or estimable, but the underlying information is incomplete (e.g., n missing for some timepoints, only percentages reported without full counts). Document the limitation in {traj_attrition_info} # NA: no usable information to extract or calculate an attrition rate (e.g., only baseline n or only a single later n reported, no longitudinal sample-size information available) | mandatory | string | nominal | drop-down list (single) | {author_reported, calculated_baseline_reference, calculated_non_baseline_reference, partial_information} | NA | NA | NA | calculated_baseline_reference | NA | |||
| traj_attrition_category | no | lev2_trajectory_models | categorization of attrition rate | # categorize {traj_attrition_rate} as: ## ≤20% → low ## 21–40% → moderate ## 41–60% → high ## >60% → very high # apply standard rounding rules before categorization # if {traj_attrition_rate} = NA: code NA | mandatory | string | ordinal | input field | {low, moderate, high, very_high} | NA | NA | {attrition_rate} | low | NA | |||
| traj_tp_comments | lev2_trajectory_models | free-text notes on measurement timepoint and attrition coding decisions | # use to document special cases, additional information and data sources, and deviations from standard extraction rules # avoid duplicating structured fields | context-dependent | string | nominal | input field | NA | NA | NA | NA | NA | |||||
| Level 2.4: Modeling | lev2_trajectory_models | ||||||||||||||||
| traj_model_software | no | lev2_trajectory_models | primary statistical software used to estimate the trajectory model | # code only the primary software used for trajectory model estimation, not software used for other analyses such as descriptive statistics, preprocessing, regressions, or visualization # if an interface or wrapper was used, code the software that performed the model estimation, e.g., code Mplus when Mplus models were run via R/MplusAutomation # if multiple software programs were used for the same trajectory model, code the main estimation software and document ambiguity in {traj_model_comments} if needed # if multiple software programs are reported for the study but the software used for trajectory model estimation cannot be determined, do not infer the estimation software from software used for other analyses; code NA and document the reported software in {traj_model_software_other} | mandatory | string | nominal | drop-down list (single) | {Mplus, SAS, R, Stata, Latent_GOLD, other} | NA | NA | NA | NA | ||||
| traj_model_software_other | no | lev2_trajectory_models | primary statistical software used to estimate the trajectory model when {traj_model_software} = other | NA | conditional: {traj_model_software} = other | string | nominal | input field | NA | NA | NA | NA | NA | ||||
| traj_model_software_version | no | lev2_trajectory_models | reported version of the statistical software used for trajectory model estimation | NA | mandatory | float | nominal | input field | NA | NA | NA | NA | NA | ||||
| traj_model_software_tool | no | lev2_trajectory_models | specific package, procedure, module, function, or built-in modeling framework used to estimate the trajectory model | # for standalone software without a separate package or procedure structure, code the relevant built-in modeling framework, e.g., Mplus_mixture_modeling or Latent_GOLD # for R, SAS, or Stata, code the reported package, procedure, function, or plugin where available, e.g., R_lcmm, SAS_PROC_TRAJ, or Stata_traj # specific auxiliary commands or options such as Mplus R3STEP or KNOWNCLASS are not coded here, because they describe auxiliary-variable handling or model options rather than the primary modeling tool | mandatory | string | nominal | drop-down list (single) | {Mplus_mixture_modeling, SAS_PROC_TRAJ, SAS_PROC_LCA, R_lcmm, R_hlme, R_multlcmm, R_kml, R_kml3d, R_flexmix, R_mclust, R_poLCA, R_tidyLPA, Stata_traj, Stata_gsem, Latent_GOLD, other} | NA | NA | NA | NA | ||||
| traj_model_software_tool_other | no | lev2_trajectory_models | specific package, procedure, module, function, or built-in modeling framework used to estimate the trajectory model when when {traj_model_software_tool} = other | NA | conditional: {traj_model_software_tool} = other | string | nominal | input field | NA | NA | NA | NA | NA | ||||
| traj_analysis_code_status | no | lev2_trajectory_models | public availability and retrievability status of analysis code or syntax for the trajectory model | CATEGORIES # publicly_retrievable: code can be directly accessed by the coder through a public supplement, appendix, repository, or equivalent source # available_upon_request: authors state that code is available from the authors upon request, but no publicly accessible file or link is provided # reported_but_not_retrievable: authors indicate that code should be available, but the coder cannot retrieve it, for example because the link is broken, access is restricted, the repository is empty, or the referenced file is missing # code_not_reported: the publication and available supplementary materials do not mention analysis code/syntax availability # unclear: the available information is insufficient to determine whether the relevant trajectory-analysis code/syntax is available CODING RULES # Step 1: check the paper for any explicit statements about code availability; also check all referenced repositories or project pages (e.g., OSF, GitHub) to determine whether analysis code is available # Step 2: if no code is identified based on information provided in the paper, independently search for associated OSF or GitHub projects using the search string: "[paper title] OSF OR GitHub" # code availability must be explicitly stated or directly verifiable from the referenced or independently identified repository # do not infer code availability from statements that data or study materials are available upon request; a data availability statement alone does not indicate that analysis code is available | mandatory | string | nominal | drop-down list (single) | {publicly_retrievable, available_upon_request, reported_but_not_retrievable, not_reported, unclear} | NA | NA | NA | NA | ||||
| traj_analysis_code_reference | no | lev2_trajectory_models | retrieval information for the available analysis code or syntax | # enter the repository URL/DOI, supplement file name, appendix name, or other precise retrieval information # code NA if {traj_analysis_code_status} = code_not_reported | conditional: {traj_analysis_code} = FALSE | string | nominal | input field | NA | NA | NA | NA | NA | ||||
| traj_model_name | yes | lev2_trajectory_models | name used by study authors for the trajectory model underlying the extracted prevalence estimates, including the abbreviation if reported | # extract the full model name and abbreviation verbatim, if both are reported, e.g., latent class growth analysis (LCGA) # preserve the authors’ terminology, spelling, capitalization, and level of specificity # if only the full name or only an abbreviation is reported, extract the available term # do not infer a model name solely from the software, syntax, estimation procedure, or model characteristics # code NA if no model name or abbreviation is reported SELECTION RULES FOR MULTIPLE MODELS # if prevalence estimates are reported for only one trajectory model, extract the name of that model # if prevalence estimates are reported for multiple trajectory models, extract the name of the model designated by the authors as primary, final, preferred, or best fitting # if the authors do not explicitly designate a primary model, extract the name of the model underlying the prevalence estimates selected for extraction according to the general model-selection rules [ggf. ausführen bei Bedarf] # document additionally reported or estimated trajectory models in traj_model_comments, e.g., ""authors additionally estimated an LCGA model" | mandatory | string | nominal | input field | NA | NA | NA | NA | Group-based trajectory modelling (GBTM) | pending | |||
| traj_model_paradigm | no | lev2_trajectory_models | broad modeling paradigm used to identify trajectory groups | # see supporting manual | mandatory | string | nominal | drop-down list (single) | {finite_mixture_model, cluster_analysis, other} | NA | NA | NA | finite_mixture_model | pending | |||
| traj_model_fmm_type | no | lev2_trajectory_models | specifies whether the finite mixture model identified latent classes ia growth (intercept/slope) or via repeated-measures response patterns | # see supporting manual # code NA if the model is not a finite mixture model, or the reported information is insufficient | conditional: {traj_model_paradigm} = finite_mixture_model | string | nominal | drop-down list (single) | {growth_based, pattern_based} | NA | NA | NA | growth_based | pending | |||
| traj_model_fmm_growth_label | no | lev2_trajectory_models | harmonized label for the growth-based finite mixture model reflecting within-class growth-factor variance structure | # see supporting manual # code NA if model is not a growth-based finite mixture model | conditional: {traj_model_fmm_type} = growth_based | string | nominal | drop-down list (single) | {lcga_gbtm, gmm, restricted_gmm, variance_structure_unclear, other} | NA | NA | NA | lcga_gbtm | pending | |||
| traj_model_fmm_pattern_label | no | lev2_trajectory_models | harmonized label for pattern-based finite mixture models | # repeated_measures_lca = pattern-based finite mixture model in which latent classes are estimated from repeated categorical outcome indicators across time points; includes longitudinal LCA and repeated-measures LCA # repeated_measures_lpa = pattern-based finite mixture model in which latent profiles/classes are estimated from repeated continuous outcome indicators across time points; includes longitudinal LPA and repeated-measures LPA # factor_mixture_model = pattern-based finite mixture model that combines a categorical latent class variable with one or more continuous latent factors; use when the model is reported as factor mixture analysis/modeling and is used to identify longitudinal response/profile patterns # other = another pattern-based finite mixture model specification that does not fit the categories above # NA = not applicable because the model is not a pattern-based finite mixture model, or because the reported information is insufficient to assign a more specific pattern-based model label | conditional: {traj_model_fmm_type} = pattern_based | string | nominal | drop-down list (single) | {repeated_measures_lca, repeated_measures_lpa, factor_mixture_model, other} | NA | NA | NA | repeated_measures_lca | pending | |||
| traj_model_other | no | lev2_trajectory_models | free-text description of the trajectory modeling approach if it does not fall under the predefined model categories | # short free-text description of modeling approach | conditional: {traj_model_paradigm, traj_model_fmm_growth_label, traj_model_fmm_pattern_label} = other | string | nominal | input field | NA | NA | NA | NA | pending | ||||
| traj_model_measurement_structure | no | lev2_trajectory_models | measurement or indicator structure of the construct underlying the trajectory model | # observed_composite_score = the trajectory model is based on observed continuous or approximately continuous scale, subscale, sum, mean, factor-score, or index scores at each time point (e.g., PTSD total scores, depression sum scores) # observed_single_item = the trajectory model is based on one observed item or single rating at each time point # observed_categorical_indicator = the trajectory model is based on observed categorical, ordinal, binary, diagnostic, or class/status indicators at each time point (e.g., symptom presence/absence or diagnostic status) # latent_measurement_model = the trajectory construct is represented by latent measurement factors rather than directly observed scale scores; includes second-order growth models and latent growth curve mixture models with longitudinal measurement models, such as LGCMM or SOGMM # mixed = the same trajectory model combines more than one measurement/indicator structure, for example observed continuous scores and categorical indicators, or observed scores and latent measurement factors # NA = the measurement/indicator structure used in the trajectory model is not reported or cannot be inferred | mandatory | string | nominal | drop-down list (single) | {observed_composite_score, observed_single_item, observed_categorical_indicator, latent_measurement_model, mixed} | NA | NA | NA | observed_composite_score | NA | |||
| traj_model_extraction_model | no | lev2_trajectory_models | indicates whether the trajectory model from which growth-factor specifications, trajectory labels, class sizes, and prevalence estimates were extracted was unconditional or conditional | CATEGORIES # unconditional = extracted information refers to a trajectory model without covariates included in the trajectory model itself # conditional = extracted information refers to a trajectory model in which covariates were included in the trajectory model itself, for example as predictors of growth factors, repeated outcome values, or class membership during model estimation # mixed = different pieces of extracted model information come from different model versions, for example growth-factor specifications from an unconditional model but class sizes or prevalence estimates from a conditional model # NA = no usable information is reported to determine whether the extracted model was unconditional or conditional CODING RULES # code the model version from which the main extracted trajectory information was taken, including trajectory labels, class sizes, prevalence estimates, and, where applicable, growth-factor variance/covariance specifications. # if both unconditional and conditional models are reported, prioritize the conditional model only when the required trajectory specifications and prevalence estimates are reported for that model. # if only the unconditional model provides the required trajectory specifications and prevalence estimates, extract information from the unconditional model and code unconditional # do not code conditional only because predictors of class membership were examined after trajectory classes had already been identified, for example in a separate three-step procedure or post-hoc multinomial regression, unless the extracted trajectory solution or prevalence estimates explicitly refer to that conditional model # use mixed_model_sources when core extracted information had to be taken from different model versions. Document which information came from which model in traj_model_comments | mandatory | string | nominal | drop-down list (single) | {unconditional, conditional, mixed_model_sources} | NA | NA | NA | conditional | NA | |||
| gf_intercept_variance_level | no | lev2_trajectory_models | highest degree of freedom allowed for within-class intercept variance in the final growth-based finite mixture model | # see supporting manual # code NA if model is not a growth-based finite mixture model | conditional: {traj_model_fmm_type} = growth_based | string | nominal | drop-down list (single) | {estimated_class_specific, estimated_constrained_equal_across_classes, estimated_constraint_unclear, fixed_to_0, unclear} | NA | NA | NA | estimated_class_specific | pending | |||
| gf_intercept_variance_pattern | no | lev2_trajectory_models | describes whether the intercept variance treatment was uniform or mixed across latent classes | # see supporting manual # code NA if model is not a growth-based finite mixture model | conditional: {traj_model_fmm_type} = growth_based | string | nominal | drop-down list (single) | {uniform_fixed_to_0, uniform_equal_across_classes, uniform_class_specific, mixed_class_specific_and_fixed_to_0, mixed_class_specific_and_equal_across_classes, mixed_fixed_to_0_and_equal_across_classes, mixed_all_three, unclear} | NA | NA | NA | uniform_class_specific | pending | |||
| gf_slope_variance_level | no | lev2_trajectory_models | highest degree of freedom allowed for within-class variance in slope or other change-related growth factors in the final growth-based finite mixture model | # see supporting manual # code NA if model is not a growth-based finite mixture model | conditional: {traj_model_fmm_type} = growth_based | string | nominal | drop-down list (single) | {estimated_class_specific, estimated_constrained_equal_across_classes, estimated_constraint_unclear, fixed_to_0, unclear} | NA | NA | NA | estimated_class_specific | pending | |||
| gf_slope_variance_pattern | no | lev2_trajectory_models | describes whether slope or change-related growth-factor variances wer uniform or mixed across latent classes and/or across change terms | # see supporting manual # code NA if model is not a growth-based finite mixture model | conditional: {traj_model_fmm_type} = growth_based | string | nominal | drop-down list (single) | {uniform_fixed_to_0, uniform_equal_across_classes, uniform_class_specific, mixed_class_specific_and_fixed_to_0, mixed_class_specific_and_equal_across_classes, mixed_fixed_to_0_and_equal_across_classes, mixed_all_three, unclear} | NA | NA | NA | uniform_class_specific | pending | |||
| gf_covariance_level | no | lev2_trajectory_models | highest degree of freedom allowed for covariances among latent growth factors in the final growth-based finite mixture model | # see supporting manual # code NA if model is not a growth-based finite mixture model, or if fewer than two latent growth factors are specified | conditional: {traj_model_fmm_type} = growth_based | string | nominal | drop-down list (single) | {estimated_class_specific, estimated_constrained_equal_across_classes, estimated_constraint_unclear, fixed_to_0, not_estimable_relevant_variance_fixed_to_0, unclear} | NA | NA | NA | estimated_class_specific | pending | |||
| gf_spec_basis | no | lev2_trajectory_models | primary evidentiary basis for the growth-factor variance and covariance specifications of the final growth-based finite mixture model | # see supporting manual # code NA if model is not a growth-based finite mixture model, or if fewer than two latent growth factors are specified | conditional: {traj_model_fmm_type} = growth_based | string | nominal | drop-down list (single) | {syntax_or_model_command, model_diagram_or_path_specification, explicit_verbal_description, parameter_estimate_table, deduced_from_reported_results, deduced_from_overall_methods_description, conservatively_inferred_software_default, conservatively_inferred_author_model_label, no_usable_information} | NA | NA | NA | syntax_or_model_command | pending | |||
| traj_model_n_reported | yes | lev2_trajectory_models | sample size reported for modeling | # extract the reported total analytic sample size used for the trajectory model # if multiple trajectory models are reported with different sample sizes, use the N of the prevalence-defining model # If no total trajectory-model N is reported, code NA | mandatory | integer | discrete | input field | [150, ∞] | NA | NA | 1735 | NA | ||||
| traj_model_n_sum | no | lev2_trajectory_models | sum of trajectory-specific sample sizes | NA | mandatory | integer | discrete | input field | [150, ∞] | NA | NA | 1735 | NA | ||||
| traj_model_n_equal | no | lev2_trajectory_models | indicates whether the total sample size reported for the trajectory model equals the sum of the trajectory-specific sample sizes | NA | mandatory | boolean | binary | drop-down list (single) | {TRUE, FALSE} | NA | NA | true | pending | ||||
| traj_model_n_analyses | no | lev2_trajectory_models | harmonized model sample size used for quantitative analyses | # usually identical to {traj_model_n_reported} # if trajectory-specific Ns were derived from percentages, use {traj_model_n_reported}; do not adjust based on discrepancies caused by rounding or limited decimal precision # if both the total model N and trajectory-specific Ns were reported but differ, use {traj_model_n_sum} as the harmonized denominator, document the discrepancy in {traj_results_consistency_desc}, and flag the case in {traj_results_consistency} # contact study authors if the discrepancy is substantial, cannot be explained by rounding/reporting format, or affects trajectory prevalence estimates | |||||||||||||
| traj_model_comments | no | lev2_trajectory_models | comments concerning modelling approach | NA | context-dependent | string | nominal | input field | NA | NA | NA | n modelling = conditional model | NA | ||||
| Level 2.4: Trajectories | lev2_trajectory_models | ||||||||||||||||
| traj_num | yes | lev2_trajectory_models | number of trajectories identified in the final selected trajectory model | # extract the number of trajectories identified in the final selected model | mandatory | integer | discrete | input field | [2; ∞] | NA | NA | NA | 5 | NA | |||
| traj_means_source | no | lev2_trajectory_models | source from which trajectory-specific values or equivalent trajectory-level information materially used for trajectory characterization and harmonized labeling were obtained | CATEGORIES # numeric_values_only: class-specific values or model-estimated means are explicitly reported numerically in text, tables, or supplementary materials and are sufficient for classification # model_parameters_only: the trajectory course is derived from reported model parameters, such as intercepts, slopes, or higher-order growth terms, rather than directly reported timepoint-specific values # figure_only: classification relies on visual estimation of the trajectory from a figure, without additional numeric or verbal information materially affecting the classification # verbal_description_only: trajectory level or course is derived solely from a substantive verbal description because usable numeric values, figures, or directly interpretable model parameters are unavailable # mixed: two or more source types are materially required for trajectory characterization or harmonized labeling CODING RULES # code the source materially used for classification, not all sources available in the study # if numeric values are sufficient and a figure is used only for confirmation, code numeric_values_only # prioritize usable numeric values over visual figure estimation when sources conflict; document substantive conflicts in {traj_comments} # code verbal_description_only only when the text provides substantive information about trajectory level or course, such as “symptoms remained below the clinical cut-off,” “the class showed persistently elevated symptoms,” or “symptoms increased over time”. An author-assigned trajectory name alone, such as “resilient,” “chronic,” or “recovery,” is not sufficient # if a verbal description only confirms a classification already fully supported by numeric values, a figure, or model parameters, retain the corresponding _only category # if the verbal description provides information required for classification in addition to another source, code mixed # if no usable numeric, graphical, or model-parameter information is available, code NA | context-dependent | string | nominal | drop-down list (single) | {numeric_values_only, mixed, verbal_description_only, figure_only, model_parameters_only} | NA | NA | NA | values | pending | |||
| traj_severity_basis | no | lev2_trajectory_models | highest-priority evidence tier that materially determined the severity-band assignments used for harmonized trajectory labeling | # see supporting manual # if no usable information is available, code NA | context-dependent | string | nominal | drop-down list (single) | {culture_language_specific_validated_threshold, resimeta_dictionary_guidance, other_validated_guidance, author_interpretation, sample_relative_fallback, no_defensible_severity_interpretation} | NA | NA | NA | resimeta_dictionary_guidance | pending | |||
| traj_subclinical_band_basis | no | lev2_trajectory_models | basis used to define the subclinical severity band applied during harmonized trajectory labeling | # see supporting manual # if no subclinical band was assigned or required, code NA | context-dependent | string | nominal | drop-down list (single) | {multi_band_validated_thresholds, upper_quarter_heuristic} | NA | NA | NA | multi_band_validated_threshold | pending | |||
| traj_cutoff_clinical | no | lev2_trajectory_models | numeric score boundary used to distinguish the clinical severity band from lower severity bands | # record the numeric boundary on the exact score metric used for trajectory labeling # the applicable comparison direction and boundary inclusivity follow the selected threshold source and must be documented in the ResiMETA instrument dictionary or, for tier 1 and tier 3, in {traj_comments} # for higher-is-worse outcomes, values above the applicable clinical boundary indicate greater severity; for higher-is-better outcomes, values below the applicable clinical boundary indicate greater impairment # record the numeric threshold stated in the applicable source irrespective of whether the boundary is expressed using an inclusive or exclusive comparison operator (example: if the source defines the cut-off as >=10 or as >10, record 10 in both cases); boundary inclusivity and the applicable comparison operator must remain documented in the ResiMETA instrument dictionary or, for tier 1 and tier 3 thresholds, in {traj_comments} # code NA when no explicit clinical boundary is applied; e.g., by default for`{traj_severity_basis} ∈ {clinically_anchored_author_interpretation, sample_relative_fallback, or no_defensible_severity_interpretation} (tiers 4–6), because no tier 1–3 cut-off is applied | context-dependent | float | continuous | input field | [0; ∞] | NA | 0-2 depending on instrument characteristics | NA | 5 | pending | |||
| traj_cutoff_subclinical | no | lev2_trajectory_models | numeric score boundary used to distinguish the subclinical severity band from the non-clinical severity band | # record the numeric boundary on the exact score metric used for trajectory labeling # when the ResiMETA single-cut-off heuristic is applied, record the calculated upper-quarter boundary # the applicable comparison direction and boundary inclusivity follow the selected threshold source and must be documented in the ResiMETA instrument dictionary or, for Tier 1 and Tier 3, in {traj_comments} # record the numeric threshold stated in the applicable source irrespective of whether the boundary is expressed using an inclusive or exclusive comparison operator (example: if the source defines the cut-off as >=10 or as >10, record 10 in both cases); boundary inclusivity and the applicable comparison operator must remain documented in the ResiMETA instrument dictionary or, for Tier 1 and Tier 3 thresholds, in {traj_comments} # code NA when no explicit clinical boundary is applied; e.g., by default for`{traj_severity_basis} ∈ {clinically_anchored_author_interpretation, sample_relative_fallback, or no_defensible_severity_interpretation} (tiers 4–6), because no tier 1–3 cut-off is applied | context-dependent | float | continuous | input field | [0; ∞] | NA | 0-2 depending on instrument characteristics | NA | 3.5 | pending | |||
| traj_[1-n]_name | outdated version | lev2_trajectory_models | name assigned by the study authors to the identified trajectory | # extract the trajectory name verbatim # if multiple names are used for the same trajectory, prefer the most substantively informative label describing its level or change pattern (e.g., prefer “low symptoms declining” over “group 2”) # do not replace the author-provided name with the harmonized trajectory label # if no trajectory name is reported, code NA | context-dependent | string | nominal | input field | NA | NA | NA | NA | no depression stable (very low) | NA | |||
| traj_[1-n]_pct | outdated version | lev2_trajectory_models | trajectory-specific percentage | # extract the trajectory-specific percentage for the final selected model # if both a model-estimated class proportion and a percentage based on assigned class membership are reported, prefer the model-estimated proportion (see supporting manual); document its basis in {traj_n_basis} # if only one trajectory-specific percentage is reported, extract the reported value regardless of basis and document its basis in {traj_pct_basis} # if no percentage is reported but the trajectory-specific N and model N are available, calculate: trajectory percentage = trajectory N / model N × 100 and document the provenance in {traj_pct_basis} # use the analytical sample size of the model from which the trajectories and prevalences are extracted # retain the number of decimal places used in the publication # round self-calculated percentages to two decimal places # prefer an applicable reported percentage over a self-calculated percentage # if reported percentage and trajectory-specific N have different statistical bases (e.g., model-estimated percentage and assigned class N), retain both as reported and do not force them to correspond | mandatory | float | continuous | input field | [0; 100] | percentage | variable | NA | 27.2 | pending | |||
| traj_[1-n]_n | outdated version | lev2_trajectory_models | number of participants assigned to the identified trajectory | # extract the trajectory-specific absolute N for the final selected model # if both an assigned class N and a model-estimated/expected class N are reported, prefer the assigned class N (see supporting manual); document its basis in {traj_n_basis} # if only one trajectory-specific N is reported, extract the reported value regardless of basis and document its basis in {traj_n_basis} # if no absolute N is reported but the trajectory percentage and model N are available, calculate: trajectory N = trajectory percentage / 100 × model N and document the provenance in {traj_n_basis} # round a calculated trajectory N to the nearest whole number using standard rounding # prefer an applicable reported absolute N over a derived N # a trajectory N derived from a model-estimated percentage represents a prevalence-equivalent/expected count and not necessarily the number of participants assigned to that class # do not adjust derived trajectory Ns solely to force their sum to equal the model N, as discrepancies may result from rounded percentages or from the statistical basis of the source proportions # if reported percentage and trajectory-specific N have different statistical bases, retain both as reported and do not force them to correspond # this field may be automatically calculated by default, but remove formula after checking and always retain the applicable value reported in the publication where reported | mandatory | integer | discrete | input field | NA | NA | NA | NA | 471 | pending | |||
| traj_[1-n]_level_last_pre | no | lev2_trajectory_models | severity band at the last modeled pre-stressor assessment | # see supporting manual # if no pre-stressor assessment was modeled, code NA # if sample-relative fallback was used instead of an absolute severity-band assignment, code NA | conditional: {traj_severity_basis} = ∈ {paper_specific_validated_threshold, resiMETA_dictionary_threshold, other_validated_interpretive_guidance, author_interpretation} | string | nominal | drop-down list (single) | {non_clinical, subclinical, clinical, unclear} | NA | NA | NA | non_clinical | pending | |||
| traj_[1-n]_level_first_post | no | lev2_trajectory_models | severity band at the first modeled post-stressor assessment | # see supporting manual # use the first modeled assessment occurring after the defined index stressor # if sample-relative fallback was used instead of an absolute severity-band assignment, code NA | conditional: {traj_severity_basis} = ∈ {paper_specific_validated_threshold, resiMETA_dictionary_threshold, other_validated_interpretive_guidance, author_interpretation} | string | nominal | drop-down list (single) | {non_clinical, subclinical, clinical, unclear} | NA | NA | NA | non_clinical | pending | |||
| traj_[1-n]_level_last_post | no | lev2_trajectory_models | severity band at the last modeled post-stressor assessment | # see supporting manual # use the last modeled assessment occurring after the defined index stressor # if sample-relative fallback was used instead of an absolute severity-band assignment, code NA | conditional: {traj_severity_basis} = ∈ {paper_specific_validated_threshold, resiMETA_dictionary_threshold, other_validated_interpretive_guidance, author_interpretation} | string | nominal | drop-down list (single) | {non_clinical, subclinical, clinical, unclear} | NA | NA | NA | non_clinical | pending | |||
| traj_[1-n]_main_change | no | lev2_trajectory_models | broad post-stressor change pattern based on transitions between assigned severity bands | # see supporting manual # if sample-relative fallback was used instead of an absolute severity-band assignment, code NA | conditional: {traj_severity_basis} = ∈ {paper_specific_validated_threshold, resiMETA_dictionary_threshold, other_validated_interpretive_guidance, author_interpretation} | string | nominal | drop-down list (single) | {stable, net_decreasing, net_increasing, non_monotonic_up_then_down, non_monotonic_down_then_up, non_monotonic_multi_reversal, unclear} | NA | NA | NA | non_clinical | pending | |||
| traj_[1-n]_label | no | lev2_trajectory_models | harmonized trajectory label assigned according to the resiMETA trajectory labeling manual | # see supporting manual | mandatory | string | nominal | drop-down list (single) | {resilient, moderate_stable, chronic_distress, recovery, delayed_distress, curvilinear, unclear} | NA | NA | NA | resilient | pending | |||
| traj_resilient | no | lev2_trajectory_models | indicates whether the final selected trajectory model contains at least one trajectory assigned the harmonized label resilient | NA | context-dependent | boolean | nominal | input field | {TRUE, FALSE} | NA | NA | pending | true | NA | |||
| traj_resilient_count | no | lev2_trajectory_models | number of trajectories in the final selected trajectory model assigned the harmonized label resilient | # if no resilient trajectory was identified, code NA | context-dependent | integer | discrete | input field | [1; ∞] | NA | NA | pending | 2 | NA | |||
| traj_resilient_name | no | lev2_trajectory_models | author-provided name or names of the trajectories assigned the harmonized label resilient | # if multiple resilient trajectories were identified, record all author-provided names in trajectory-number order, separated by ", " # if no resilient trajectory was identified, code NA | context-dependent | string | nominal | input field | NA | NA | NA | pending | low-stable | NA | |||
| traj_resilient_n | no | lev2_trajectory_models | total number of participants assigned to trajectories with the harmonized label resilient | # for one resilient trajectory, extract the corresponding traj_[i]_n # if multiple resilient trajectories were identified, sum their trajectory-specific Ns # if no resilient trajectory was identified, code NA | context-dependent | integer | discrete | input field | [1; ∞] | NA | NA | pending | 100 | NA | |||
| traj_resilient_pct | no | lev2_trajectory_models | total percentage of participants assigned to trajectories with the harmonized label resilient | # calculate from the total number of participants assigned to resilient trajectories and the analytical sample size of the extracted trajectory model: {traj_resilient_pct} = {traj_resilient_n} / {traj_model_n_analyses} × 100 # if multiple resilient trajectories were identified, first sum their trajectory-specific Ns to obtain traj_resilient_n # round the calculated percentage to two decimal places # if no resilient trajectory was identified, code NA | context-dependent | float | continuous | input field | [0.01; 100] | percentage | 2 | pending | 5.55 | NA | |||
| traj_recovery | no | lev2_trajectory_models | indicates whether the final selected trajectory model contains at least one trajectory assigned the harmonized label recovery | NA | context-dependent | boolean | nominal | input field | {TRUE, FALSE} | NA | NA | pending | true | NA | |||
| traj_recovery_count | no | lev2_trajectory_models | number of trajectories in the final selected trajectory model assigned the harmonized label recovery | # if no recovery trajectory was identified, code NA | context-dependent | integer | discrete | input field | [1; ∞] | NA | NA | pending | 2 | NA | |||
| tra_recovery_name | no | lev2_trajectory_models | author-provided name or names of the trajectories assigned the harmonized label recovery | # if multiple recovery trajectories were identified, record all author-provided names in trajectory-number order, separated by ", " # if no recovery trajectory was identified, code NA | context-dependent | string | nominal | input field | NA | NA | NA | pending | recovered | NA | |||
| traj_recovery_n | no | lev2_trajectory_models | total number of participants assigned to trajectories with the harmonized label recovery | # for one recovery trajectory, extract the corresponding traj_[i]_n # if multiple recovery trajectories were identified, sum their trajectory-specific Ns # if no recovery trajectory was identified, code NA | context-dependent | integer | discrete | input field | [1; ∞] | NA | NA | pending | 100 | NA | |||
| traj_recovery_pct | no | lev2_trajectory_models | total percentage of participants assigned to trajectories with the harmonized label recovery | # calculate from the total number of participants assigned to recovery trajectories and the analytical sample size of the extracted trajectory model: {traj_recovery_pct} = {traj_recovery_n} / {traj_model_n_analyses} × 100 # if multiple recovery trajectories were identified, first sum their trajectory-specific Ns to obtain traj_recovery_n # round the calculated percentage to two decimal places # if no recoverytrajectory was identified, code NA | context-dependent | float | continuous | input field | [0.01; 100] | percentage | 2 | pending | 5.55 | NA | |||
| traj_delayed | no | lev2_trajectory_models | indicates whether the final selected trajectory model contains at least one trajectory assigned the harmonized label delayed distress | NA | context-dependent | boolean | nominal | input field | {TRUE, FALSE} | NA | NA | pending | true | NA | |||
| traj_delayed_count | no | lev2_trajectory_models | number of trajectories in the final selected trajectory model assigned the harmonized label delayed distress | # if no delayed distress trajectory was identified, code NA | context-dependent | integer | discrete | input field | [1; ∞] | NA | NA | pending | 2 | NA | |||
| tra_delayed_name | no | lev2_trajectory_models | author-provided name or names of the trajectories assigned the harmonized label delayed distress | # if multiple delayed distress trajectories were identified, record all author-provided names in trajectory-number order, separated by ", " # if no delayed distress trajectory was identified, code NA | context-dependent | string | nominal | input field | NA | NA | NA | pending | delayed dysfunction | NA | |||
| traj_delayed_n | no | lev2_trajectory_models | total number of participants assigned to trajectories with the harmonized label delayed distress | # for one delayed distress trajectory, extract the corresponding traj_[i]_n # if multiple delayed distress trajectories were identified, sum their trajectory-specific Ns # if no delayed distress trajectory was identified, code NA | context-dependent | integer | discrete | input field | [1; ∞] | NA | NA | pending | 100 | NA | |||
| traj_delayed_pct | no | lev2_trajectory_models | total percentage of participants assigned to trajectories with the harmonized label delayed distress | # calculate from the total number of participants assigned to delayed distress trajectories and the analytical sample size of the extracted trajectory model: {traj_delayed_pct} = {traj_delayed_n} / {traj_model_n_analyses} × 100 # if multiple delayed distress trajectories were identified, first sum their trajectory-specific Ns to obtain traj_delayed_n # round the calculated percentage to two decimal places # if no delayed distress trajectory was identified, code NA | context-dependent | float | continuous | input field | [0.01; 100] | percentage | 2 | pending | 5.55 | NA | |||
| traj_chronic | no | lev2_trajectory_models | indicates whether the final selected trajectory model contains at least one trajectory assigned the harmonized label chronic distress | NA | context-dependent | boolean | nominal | input field | {TRUE, FALSE} | NA | NA | pending | true | NA | |||
| traj_chronic_count | no | lev2_trajectory_models | number of trajectories in the final selected trajectory model assigned the harmonized label chronic distress | # if no chronic distress trajectory was identified, code NA | context-dependent | integer | discrete | input field | [1; ∞] | NA | NA | pending | 2 | NA | |||
| tra_chronic_name | no | lev2_trajectory_models | author-provided name or names of the trajectories assigned the harmonized label chronic distress | # if multiple chronic distress trajectories were identified, record all author-provided names in trajectory-number order, separated by ", " # if no chronic distress trajectory was identified, code NA | context-dependent | string | nominal | input field | NA | NA | NA | pending | persistently high k6, persistently mild k6 | NA | |||
| traj_chronic_n | no | lev2_trajectory_models | total number of participants assigned to trajectories with the harmonized label chronic distress | # for one chronic distress trajectory, extract the corresponding traj_[i]_n # if multiple chronic distress trajectories were identified, sum their trajectory-specific Ns # if no chronic distress trajectory was identified, code NA | context-dependent | integer | discrete | input field | [1; ∞] | NA | NA | pending | 100 | NA | |||
| traj_chronic_pct | no | lev2_trajectory_models | total percentage of participants assigned to trajectories with the harmonized label chronic distress | # calculate from the total number of participants assigned to chronic distress trajectories and the analytical sample size of the extracted trajectory model: {traj_chronic _pct} = {traj_chronic _n} / {traj_model_n_analyses} × 100 # if multiple chronic distress trajectories were identified, first sum their trajectory-specific Ns to obtain traj_chronic _n # round the calculated percentage to two decimal places # if no chronic distress trajectory was identified, code NA | context-dependent | float | continuous | input field | [0.01; 100] | percentage | 2 | pending | 5.55 | NA | |||
| traj_moderate_stable | no | lev2_trajectory_models | indicates whether the final selected trajectory model contains at least one trajectory assigned the harmonized label moderate stable distress | NA | context-dependent | boolean | nominal | input field | {TRUE, FALSE} | NA | NA | pending | true | NA | |||
| traj_moderate_stable_count | no | lev2_trajectory_models | number of trajectories in the final selected trajectory model assigned the harmonized label moderate stable distress | # if no moderate stable distress trajectory was identified, code NA | context-dependent | integer | discrete | input field | [1; ∞] | NA | NA | pending | 2 | NA | |||
| tra_moderate_stable_name | no | lev2_trajectory_models | author-provided name or names of the trajectories assigned the harmonized label moderate stable distress | # if multiple moderate stable distress trajectories were identified, record all author-provided names in trajectory-number order, separated by ", " # if no moderate stable distress trajectory was identified, code NA | context-dependent | string | nominal | input field | NA | NA | NA | pending | lower quality of life | NA | |||
| traj_moderate_stable_n | no | lev2_trajectory_models | total number of participants assigned to trajectories with the harmonized label moderate stable distress | # for one moderate stable distress trajectory, extract the corresponding traj_[i]_n # if multiple moderate stable distress trajectories were identified, sum their trajectory-specific Ns # if no moderate stable distress trajectory was identified, code NA | context-dependent | integer | discrete | input field | [1; ∞] | NA | NA | pending | 100 | NA | |||
| traj_moderate_stable_pct | no | lev2_trajectory_models | total percentage of participants assigned to trajectories with the harmonized label moderate stable distress | # calculate from the total number of participants assigned to moderate stable distress trajectories and the analytical sample size of the extracted trajectory model: {traj_moderate_stable _pct} = {traj_moderate_stable _n} / {traj_model_n_analyses} × 100 # if multiple moderate stable distress trajectories were identified, first sum their trajectory-specific Ns to obtain traj_moderate_stable _n # round the calculated percentage to two decimal places # if no moderate stable distress trajectory was identified, code NA | context-dependent | float | continuous | input field | [0.01; 100] | percentage | 2 | pending | 5.55 | NA | |||
| traj_curvilinear | no | lev2_trajectory_models | indicates whether the final selected trajectory model contains at least one trajectory assigned the harmonized label curvilinear (non-monotonic) | NA | context-dependent | boolean | nominal | input field | {TRUE, FALSE} | NA | NA | pending | true | NA | |||
| traj_unclear | no | lev2_trajectory_models | indicates whether the final selected trajectory model contains at least one trajectory assigned the harmonized label unclear | NA | context-dependent | boolean | nominal | input field | {TRUE, FALSE} | NA | NA | pending | true | NA |
A
B
C
D
E
F
G
H
I
J
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
| value_set_id | value | order | definition | ||||||
| vs_boolean | true | 1 | condition/characteristic is present | ||||||
| vs_boolean | false | 2 | condition/characteristic is absent | ||||||
| vs_report_data_type | TRAJ | 1 | trajectory data only | ||||||
| vs_report_data_type | RF | 2 | resilience-factor data only | ||||||
| vs_report_data_type | TRAJ_RF | 3 | both trajectory and resilience-factor data | ||||||
| vs_report_data_retained | TRAJ | 1 | trajectory data retained for the current primary-analysis dataset | ||||||
| vs_report_data_retained | RF | 2 | resilience-factor data retained | ||||||
| vs_report_data_retained | TRAJ_RF | 3 | both trajectory and resilience-factor data retained | ||||||
| vs_report_data_retained | none | 4 | no report data retained in the current primary-analysis dataset | ||||||
| vs_report_type | study_protocol | 1 | study protocol | ||||||
| vs_report_type | preprint | 2 | preprint | ||||||
| vs_report_type | peer_reviewed_article | 3 | peer-reviewed journal article | ||||||
| vs_report_type | conference_abstract | 4 | conference abstract | ||||||
| vs_report_type | dissertation | 5 | doctoral dissertation/thesis | ||||||
| vs_report_type | master_thesis | 6 | master thesis | ||||||
| vs_author_contact_status | no_need | 1 | no clarification required | ||||||
| vs_author_contact_status | contact_needed | 2 | clarification needed; not yet sent | ||||||
| vs_author_contact_status | contacted | 3 | request sent; no usable answer yet | ||||||
| vs_author_contact_status | answer_received | 4 | usable author clarification received | ||||||
| vs_sample_overlap_category | same_sample | 1 | all relevant report-sample records represent the same intended analytic participant sample | ||||||
| vs_sample_overlap_category | disjoint_samples | 2 | samples belong to the same dependency/source family but are known not to share participants (e.g., mothers vs. fathers) | ||||||
| vs_sample_overlap_category | nested | 3 | one analytic sample is fully contained within another | ||||||
| vs_sample_overlap_category | partial_overlap | 4 | known participant overlap; neither analytic sample fully contains the other | ||||||
| vs_sample_overlap_category | likely_overlap | 5 | participant overlap is likely but insufficiently documented to classify as nested or partial | ||||||
| vs_sample_overlap_category | unclear_overlap | 6 | shared source/dependency is known but individual-level overlap cannot be determined | ||||||
| vs_sample_overlap_category | mixed | 7 | the dependency group contains multiple substantively different non-identical relationships | ||||||
| vs_sample_overlap_same_sample_diff | none | 1 | same sample repeated without an analytically relevant difference | ||||||
| vs_sample_overlap_same_sample_diff | outcome | 2 | different modeled outcome construct or score | ||||||
| vs_sample_overlap_same_sample_diff | rf | 3 | different resilience-factor content or RF analysis | ||||||
| vs_sample_overlap_same_sample_diff | timepoints | 4 | different modeled timepoints or time window | ||||||
| vs_sample_overlap_same_sample_diff | model | 5 | different statistical trajectory-model specification | ||||||
| vs_sample_overlap_same_sample_diff | analytic_sample | 6 | same intended sample but analysis-specific effective N differs (e.g., due to missingness, attrition, outcome availability, minimum-timepoint requirements, or comparable completeness criteria) | ||||||
| vs_sample_overlap_same_sample_diff | other | 7 | other analytically relevant difference | ||||||
| vs_sample_pop_type | clinical_population | 1 | see project conventions | ||||||
| vs_sample_pop_type | general_population | 2 | see project conventions | ||||||
| vs_sample_pop_type | structural_or_institutional_context_population | 3 | see project conventions | ||||||
| vs_sample_pop_type | role_status_or_life_stage_population | 4 | see project conventions | ||||||
| vs_sample_pop_type | student_population | 5 | see project conventions | ||||||
| vs_sample_pop_type | working_population | 6 | see project conventions | ||||||
| vs_sample_pop_type | mixed | 7 | see project conventions | ||||||
| vs_sample_pop_type_mixed | clinical_population | 1 | see project conventions | ||||||
| vs_sample_pop_type_mixed | general_population | 2 | see project conventions | ||||||
| vs_sample_pop_type_mixed | structural_or_institutional_context_population | 3 | see project conventions | ||||||
| vs_sample_pop_type_mixed | role_status_or_life_stage_population | 4 | see project conventions | ||||||
| vs_sample_pop_type_mixed | student_population | 5 | see project conventions | ||||||
| vs_sample_pop_type_mixed | working_population | 6 | see project conventions | ||||||
| vs_sample_pop_structural_institutional | humanitarian_migrants | 1 | see project conventions | ||||||
| vs_sample_pop_structural_institutional | refugees | 2 | see project conventions | ||||||
| vs_sample_pop_structural_institutional | prisoners | 3 | see project conventions | ||||||
| vs_sample_pop_structural_institutional | ethnic_and_cultural_minorities | 4 | see project conventions | ||||||
| vs_sample_pop_structural_institutional | LGBTQIA+_individuals | 5 | see project conventions | ||||||
| vs_sample_pop_structural_institutional | low_income_individuals | 6 | see project conventions | ||||||
| vs_sample_pop_structural_institutional | unemployed_persons | 7 | see project conventions | ||||||
| vs_sample_pop_structural_institutional | children_in_out_of_home_care | 8 | see project conventions | ||||||
| vs_sample_pop_structural_institutional | children_in_child_protection_investigations | 9 | see project conventions | ||||||
| vs_sample_pop_structural_institutional | individuals_in_support_services_after_interpersonal_violence | 10 | see project conventions | ||||||
| vs_sample_pop_structural_institutional | former_child_soldiers | 11 | see project conventions | ||||||
| vs_sample_pop_role_status_life_stage | peripartum_women | 1 | see project conventions | ||||||
| vs_sample_pop_role_status_life_stage | peripartum_women_after_IVF | 2 | see project conventions | ||||||
| vs_sample_pop_role_status_life_stage | peripartum_men | 3 | see project conventions | ||||||
| vs_sample_pop_role_status_life_stage | peripartum_women_and_men | 4 | see project conventions | ||||||
| vs_sample_pop_role_status_life_stage | mothers | 5 | see project conventions | ||||||
| vs_sample_pop_role_status_life_stage | fathers | 6 | see project conventions | ||||||
| vs_sample_pop_role_status_life_stage | parents | 7 | see project conventions | ||||||
| vs_sample_pop_role_status_life_stage | brothers_and_sisters | 8 | see project conventions | ||||||
| vs_sample_pop_role_status_life_stage | partners | 9 | see project conventions | ||||||
| vs_sample_pop_role_status_life_stage | family_members | 10 | see project conventions | ||||||
| vs_sample_pop_role_status_life_stage | retirees | 11 | see project conventions | ||||||
| vs_sample_pop_role_status_life_stage | veterans | 12 | see project conventions | ||||||
| vs_sample_pop_working | multiprofessional_responders | 1 | see project conventions | ||||||
| vs_sample_pop_working | volunteer_responders | 2 | see project conventions | ||||||
| vs_sample_pop_working | military_personnel | 3 | see project conventions | ||||||
| vs_sample_pop_working | health_care_workers | 4 | see project conventions | ||||||
| vs_sample_pop_working | emergency_medical_services_personnel | 5 | see project conventions | ||||||
| vs_sample_pop_working | social_care_workers | 6 | see project conventions | ||||||
| vs_sample_pop_working | health_and_social_care_workers | 7 | see project conventions | ||||||
| vs_sample_pop_working | humanitarian_aid_workers | 8 | see project conventions | ||||||
| vs_sample_pop_working | law_enforcement_personnel | 9 | see project conventions | ||||||
| vs_sample_pop_working | fire_service_personnel | 10 | see project conventions | ||||||
| vs_sample_pop_working | institutional_staff | 11 | see project conventions | ||||||
| vs_sample_pop_working | corporate_employees | 12 | see project conventions | ||||||
| vs_sample_pop_working | multiple_professions | 13 | see project conventions | ||||||
| vs_sample_population_student | college_or_university_students | 1 | see project conventions | ||||||
| vs_sample_population_student | primary_or_high_school_students | 2 | see project conventions | ||||||
| vs_sample_pop_clinical | mental_health_condition | 1 | see project conventions | ||||||
| vs_sample_pop_clinical | physical_health_condition | 2 | see project conventions | ||||||
| vs_sample_pop_clinical | unclear_condition | 3 | see project conventions | ||||||
| vs_sample_age_group | young | 1 | 0–24 years | ||||||
| vs_sample_age_group | middle | 2 | 25–60 years | ||||||
| vs_sample_age_group | old | 3 | >60 years | ||||||
| vs_stressor_category | pandemics_and_epidemics | 1 | see project conventions | ||||||
| vs_stressor_category | conflicts_and_wars | 2 | see project conventions | ||||||
| vs_stressor_category | humanitarian_migration | 3 | see project conventions | ||||||
| vs_stressor_category | environmental_and_natural_disasters | 4 | see project conventions | ||||||
| vs_stressor_category | non_environmental_disasters | 5 | see project conventions | ||||||
| vs_stressor_category | adverse_childhood_experiences | 6 | see project conventions | ||||||
| vs_stressor_category | bereavement | 7 | see project conventions | ||||||
| vs_stressor_category | health_related_stressors_acute_event | 8 | see project conventions | ||||||
| vs_stressor_category | health_related_stressors_diagnosis_and_onset | 9 | see project conventions | ||||||
| vs_stressor_category | health_related_stressors_treatment_and_surgery | 10 | see project conventions | ||||||
| vs_stressor_category | health_related_stressors_in_close_connections | 11 | see project conventions | ||||||
| vs_stressor_category | perinatal_stressors | 12 | see project conventions | ||||||
| vs_stressor_category | military_stressors | 13 | see project conventions | ||||||
| vs_stressor_category | job_loss_and_retirement | 14 | see project conventions | ||||||
| vs_stressor_category | educational_transition | 15 | see project conventions | ||||||
| vs_stressor_category | marriage | 16 | see project conventions | ||||||
| vs_stressor_category | divorce | 17 | see project conventions | ||||||
| vs_stressor_category | violence_exposure_and_witnessing | 18 | see project conventions | ||||||
| vs_stressor_category | menopause | 19 | see project conventions | ||||||
| vs_stressor_category | financial_or_economic_crises | 20 | see project conventions | ||||||
| vs_stressor_category | terrorist_attacks_and_mass_shootings | 21 | see project conventions | ||||||
| vs_stressor_category | work_related_stressors | 22 | see project conventions | ||||||
| vs_stressor_category | assisted_reproductive_treatment | 23 | see project conventions | ||||||
| vs_stressor_category | other | 24 | see project conventions | ||||||
| vs_stressor_category | mixed | 25 | see project conventions | ||||||
| vs_stressor_perinatal_spec | pregnancy | 1 | pregnancy as the primary perinatal stressor | ||||||
| vs_stressor_perinatal_spec | birth | 2 | birth/delivery as the primary perinatal stressor | ||||||
| vs_stressor_perinatal_spec | perinatal_transition | 3 | pregnancy and birth treated as one perinatal transition | ||||||
| vs_stressor_level | individual | 1 | stressor primarily anchored in the index person's body, health, treatment, biological/life-course transition, or personal acute event | ||||||
| vs_stressor_level | social | 2 | stressor primarily anchored in relationships, family, interpersonal events, roles, occupational/educational/institutional contexts, or close others | ||||||
| vs_stressor_level | societal | 3 | stressor primarily anchored in population/community-level public-health, geopolitical, ecological, economic, or disaster contexts | ||||||
| vs_stressor_level | mixed | 4 | pooled index stressors span levels without one defensible primary level | ||||||
| vs_stressor_level | unclear | 5 | available information is insufficient to identify the stressor level | ||||||
| vs_stressor_chronicity | acute | 1 | see project conventions | ||||||
| vs_stressor_chronicity | chronic_homogenous | 2 | see project conventions | ||||||
| vs_stressor_chronicity | chronic_heterogenous | 3 | see project conventions | ||||||
| vs_stressor_chronicity | transitional_normative | 4 | see project conventions | ||||||
| vs_stressor_chronicity | transitional_non_normative | 5 | see project conventions | ||||||
| vs_stressor_chronicity | mixed | 6 | see project conventions | ||||||
| vs_outc_merged_type | multi_construct_composite | 1 | multiple constructs combined into one modeled outcome | ||||||
| vs_outc_merged_type | multi_tool_same_construct | 2 | multiple assessment tools combined for the same construct (e.g., two PTSD scales) | ||||||
| vs_outc_merged_type | tool_switch_over_time | 3 | assessment tool changes over modeled timepoints while the construct remains the same (e.g., scale A at baseline, scale B at follow-up) | ||||||
| vs_outc_tool_origin | standardized_instrument | 1 | assessment instrument has an identity outside the current study (e.g., developed within a prior research program, large-scale study, or as a structured module); includes full versions, short forms, subscales, single items from named instruments, and slightly adapted or reduced versions. The instrument does NOT need to be commercially distributed, widely used, or formally validated to qualify as standardized | ||||||
| vs_outc_tool_origin | study_specific_measure | 2 | measure/item/index was created for the current study | ||||||
| vs_outc_tool_origin | mixed | 3 | merged outcome contains both standardized and study-specific measurement bases | ||||||
