Toward increasing relevance of the U.S. Army’s Deployment Cycle Resilience Training: A Quality Improvement Evaluation
Bibliographic record
Abstract
Introduction: The Deployment Cycle Resilience Training (DCRT) is a rebranded and revised version of the initial deployment resilience training called Battlemind, that was in effect from 2014 to 2018. Methods: To maintain the relevance and utility of resilience training centred on the deployment cycle, the current version of DCRT was formally evaluated using mixed methodologies by the Walter Reed Army Institute of Research. Results: The evaluation team found that both soldiers and their spouses reported predominantly positive ratings for the pre-deployment and reintegration modules of the training. In addition to reporting the training to be satisfactory and relevant and reporting an intention to apply the skills beyond the deployment cycle, soldiers and spouses also identified areas for improvement related to addressing the training's relevancy and relatability. Discussion: To continue improving the training, the evaluation team recommends that the training include more examples from the army reserve and the National Guard, and that the support network (i.e., circle of support) be widened beyond spouses. These, and additional recommendations, are further discussed.
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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 | 0.184 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".