Beyond Battlemind: Evaluation of a New Mental Health Training Program for Canadian Forces Personnel Participating in Third-Location Decompression
Bibliographic record
Abstract
INTRODUCTION: Battlemind training, which improves postdeployment well-being, has been part of Canada's postdeployment Third-location Decompression (TLD) program since 2006. In 2010, a new educational program drawing on Battlemind was implemented to make it more consistent with Canada's current mental health training strategy. METHODS: Subjects consisted of 22,113 Canadian personnel returning from Afghanistan via TLD in Cyprus; 3,024 (14%) received the new program. Pre-/post-training attitude and self-efficacy questionnaires assessed the impact of the training. In addition, a quasi-experimental approach used questionnaires administered at the end of TLD to compare the satisfaction, attitudes, and self-efficacy under the old vs. new program. RESULTS: Pre-/post-training questionnaires showed medium to large positive effects of the training on targeted attitudes and self-efficacy (Cohen's d = 0.44-1.02). Participants completing the new program were more satisfied with the educational program (adjusted odds ratio = 3.2), perceived the TLD to be more valuable (odds ratio = 1.7), and had at least certain more favorable post-TLD attitudes and self-efficacy (d ranging from 0.00 to 0.29). CONCLUSION: All of these findings point to the superiority of the new program. However, quasi-experimental approaches are bias-prone, and it is unknown whether these advantages will translate into meaningful improvements in well-being.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".