Course and Predictors of Major Depressive Disorder in the Canadian Armed Forces Members and Veterans Mental Health Follow-up Survey: Cours et Prédicteurs du Trouble de Dépression Majeure Dans l’Enquête de Suivi Sur la Santé Mentale Auprès Des Membres des Forces Armées Canadiennes et des ex-Militaires
Bibliographic record
Abstract
Objectives: The present report is the first study of Canadian military personnel to use longitudinal survey data to identify factors that determine major depressive episodes (MDEs) over a period of 16 years. Methods: The study used data from the Canadian Armed Forces Members and Veterans Mental Health Follow-up Survey (CAFVMHS) collected in 2018 ( n = 2,941, response rate 68.7%) and linked baseline data from the same participants that were collected in 2002 when they were Canadian Regular Force members. The study used structured interviews to identify 5 common Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition mental disorders and collected demographic data, as well as information about traumatic experiences, childhood adversities, work stress, and potential resilience factors. Respondents were divided into 4 possible MDE courses: No Disorder, Remitting, New Onset, and Persistent/Recurrent. Relative risk ratios (RRRs) from multinomial regression models were used to evaluate determinants of these outcomes. Results: A history of anxiety disorders and post-traumatic stress disorder (RRRs: 1.50 to 20.55), mental health service utilization (RRRs: 1.70 to 12.34), veteran status (RRRs: 1.64 to 2.15), deployment-associated traumatic events (RRRs: 1.71 to 2.27), sexual traumas (RRRs: 1.91 to 2.93), other traumas (RRRs: 1.67 to 2.64), childhood adversities (RRRs: 1.39 to 1.97), avoidance coping (RRRs 1.09 to 1.49), higher frequency of religious attendance (RRRs: 1.54 to 1.61), and work stress (RRRs: 1.05 to 1.10) were associated with MDE courses in most analyses. Problem-focused coping (RRRs: 0.73 to 0.91) and social support (RRRs: 0.95 to 0.98) were associated with protection against MDEs. Conclusions: The time periods following deployment and trauma exposure and during the transition from active duty to veteran status are particularly relevant for vulnerability to depression in military members. Interventions that enhance problem-focused coping and social support may be protective against MDEs in military members.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".