Evaluating Road to Mental Readiness wellness checks for CAF search and rescue technicians
Bibliographic record
Abstract
Introduction: The Road to Mental Readiness (R2MR) program delivers mental health training, which includes a guided discussion and wellness check for Canadian Armed Forces search and rescue technicians (SAR techs) during their annual dive proficiency and currency training to support their mental health and performance and helps to decrease barriers to care and encourage care seeking when necessary. A mixed-methods evaluation of SAR techs' perceptions of the wellness checks was conducted in July 2024. Methods: Sixty SAR techs completed an anonymous survey three months post-training to capture their perceptions of the wellness checks, including their usefulness; common therapeutic factors identified as important mediators of wellness check effectiveness from previous research; and attitudes toward care-seeking, supporting others, stigma, and resilience. Results: Most SAR techs found their most recent wellness check to be at least a little helpful in supporting their mental health and well-being, and their attitudes toward the wellness check appeared to improve afterward. The learning of new skills was a common therapeutic factor that most related to perceptions of wellness check usefulness. In terms of related outcomes, those who rated the wellness check as more useful reported greater confidence in accessing mental health care, but not greater resilience or stigma. Finally, most SAR techs who received a follow-up recommendation followed through with it. Discussion: The findings indicate that SAR techs perceive the current R2MR training as useful for supporting their mental health and well-being, but the findings are limited to this unique occupation.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".