Rationale and Methodology of the 2018 Canadian Armed Forces Members and Veterans Mental Health Follow-up Survey (CAFVMHS): A 16-year Follow-up Survey: Raison D’être Et Méthodologie De L’enquête De Suivi Sur La Santé Mentale Des Membres Des Forces Armées Canadiennes Et Des Anciens Combattants, 2018 (ESSMFACM)
Bibliographic record
Abstract
Objective: Knowledge is limited regarding the longitudinal course and predictors of mental health problems, suicide, and physical health outcomes among military and veterans. Statistics Canada, in collaboration with researchers at the University of Manitoba and an international team, conducted the Canadian Armed Forces Members and Veterans Mental Health Follow-Up Survey (CAFVMHS). Herein, we describe the rationale and methods of this important survey. Method: The CAFVMHS is a longitudinal survey design with 2 time points (2002 and 2018). Regular Force military personnel who participated in the first Canadian Community Health Survey Cycle 1.2—Mental Health and Well-Being, Canadian Forces Supplement (CCHS-CFS) in 2002 ( N = 5,155) were reinterviewed in 2018 ( n = 2,941). The World Mental Health Survey–Composite International Diagnostic Interview was used with the Diagnostic and Statistical Manual of Mental Disorders, fourth edition ( DSM-IV) criteria. Results: The CAFVMHS includes 2,941 respondents (66% veterans; 34% active duty) and includes data on mental disorder diagnoses, physical health conditions, substance use, medication use, general health, mental health services, perceived need for care, social support, moral injury, deployment experiences, stress, physical activity, military-related sexual assault, childhood experiences, and military and sociodemographic information. Conclusions: The CAFVMHS provides a unique opportunity to further understand the health and well-being of military personnel in Canada over time to inform intervention and prevention strategies and improve outcomes. The data are available through the Statistics Canada Research Data Centres across Canada and can be used cross-sectionally or be longitudinally linked to the 2002 CCHS-CFS data.
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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 | 0.101 | 0.071 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".