Postdeployment military mental health training: Cross-national evaluations.
Bibliographic record
Abstract
Deployments increase risk for adjustment problems in service members. To mitigate this increased risk, mental health training programs have been developed and implemented in several nations. As part of a coordinated effort, three nations adapted a U.S. mental health training program that had been validated by a series of group randomized trials demonstrating improvement in postdeployment adjustment. Implementation of evidence-based programs in a new context is challenging: How much of the original program needs to remain intact in order to retain its utility? User satisfaction rates can provide essential data to assess how well a program is accepted. This article summarizes service member ratings of postdeployment mental health training and compares ratings from service members across four nations. The participating nations (Canada, New Zealand, United Kingdom, and the United States) administered mental health training to active duty military personnel in their respective nations. Following the training, military personnel completed an evaluation of the training. Overall, across the four nations, more than 70% of military personnel agreed or strongly agreed that they were satisfied with the mental health training. Although some differences in evaluations were observed across nations, components of training that were most important to overall satisfaction with the training were strikingly similar across nations. Fundamentally, it appears feasible that despite cultural and organizational differences, a mental health training program developed in one nation can be successfully adapted for use in other nations.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".