Exposure to mental health training and education in Canadian Armed Forces personnel
Bibliographic record
Abstract
Introduction: The Canadian Armed Forces (CAF) mental health training and education (MHTE) program seeks primarily to enhance well-being and performance through enhancement of resilience and mental health literacy. Wider dissemination of its MHTE program is a strategic priority for the CAF, but the extent of MHTE exposure and risk factors for low exposure are unknown. The objectives of this paper are (1) to describe the extent of exposure to MHTE, and (2) to explore factors associated with less exposure. Methods: The 2013 CAF Mental Health Survey ( n = 8,165) assessed exposure to MHTE in six specific training contexts. Modified Poisson regression and ordered logistic regression explored risk factors for lack of any MHTE exposure and for fewer total training hours, respectively. Results: 69.7% of respondents had exposure to MHTE over the previous 5 years. The median number of training hours in those with at least some exposure was 11 (inter-quartile range 5 to 24). Similar risk factors were identified for no MHTE exposure and for fewer MHTE hours, though the models had relatively poor predictive value. Discussion: While most CAF personnel have had at least some exposure to MHTE, the extent of exposure varies substantially, and a significant fraction have had no exposure at all. While targeting groups with low exposure identified in this analysis makes sense, the substantial variability of exposure within those groups demonstrates the need for administrative data on training exposure at the individual level on an ongoing basis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".