The 2013 Canadian Forces Mental Health Survey
Bibliographic record
Abstract
OBJECTIVE: The 2013 Canadian Forces Mental Health Survey (CFMHS) collected detailed information on mental health problems, their impacts, occupational and nonoccupational determinants of mental health, and the use of mental health services from a random sample of 8200 serving personnel. The objective of this article is to provide a firm scientific foundation for understanding and interpreting the CFMHS findings. METHODS: This narrative review first provides a snapshot of the Canadian Armed Forces (CAF), focusing on 2 key determinants of mental health: the deployment of more than 40,000 personnel in support of the mission in Afghanistan and the extensive renewal of the CAF mental health system. The findings of recent population-based CAF mental health research are reviewed, with a focus on findings from the very similar mental health survey done in 2002. Finally, key aspects of the methods of the 2013 CFMHS are presented. RESULTS: The findings of 20 peer-reviewed publications using the 2002 mental health survey data are reviewed, along with those of 25 publications from other major CAF mental health research projects executed over the past decade. CONCLUSIONS: More than a decade of population-based mental health research in the CAF has provided a detailed picture of its mental health and use of mental health services. This knowledge base and the homology of the 2013 survey with the 2002 CAF survey and general population surveys in 2002 and 2012 will provide an unusual opportunity to use the CFMHS to situate mental health in the CAF in a historical and societal perspective.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".