Improving Mental Health and Resilience Training: Feedback from Military Personnel
Bibliographic record
Abstract
Mental health and resilience training initiatives have been implemented in many military organizations with the intention of optimizing the psychological resilience of their military members. Capturing military members’ perspectives and feedback may contribute to informed decision-making and highlight opportunities for the further development and optimization of such training programs. Feedback on the existing mental health and stress exposure training (i.e., Road to Mental Readiness) was assessed through a combination of open- and closed-ended questions from an online survey of 793 actively serving Canadian Armed Forces (CAF) members. The results indicate that increasing engagement, contextual relevance, frequency, as well as making efforts to decrease the stigma surrounding mental health are commonly perceived gaps and suggestions for training improvement from the perspective of military members. Perceived gaps may not be entirely due to shortcomings of the intervention itself – there is a need for root cause analysis of subjective perceptions prior to considering program changes. Future research and program development related to resilience training can incorporate end-user feedback to not only improve the programs based on the unique needs of the target audience but also help foster a relationship between decision-makers and end-users through shared decision-making and collaboration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".