Post-deployment screening in the Canadian military: Translating evaluation research into program recommendations
Bibliographic record
Abstract
The Canadian Armed Forces' (CAF's) post-deployment screening has remained largely unchanged since its 2002 introduction. This screening is required for CAF service members returning from international deployments of more than 60 days. It consists of a questionnaire that assesses self-reported issues and is subsequently reviewed by a mental health professional interviewer. The interviewer then assesses the level of concern for the member and, if warranted, recommends follow-up care. The fundamental goal of the screening is to shorten the delay to care among those with potential mental health problems. Research to evaluate the screening program's effectiveness has been limited. This article consolidates key findings and implications from a recent evaluation project to inform those who manage and deliver the CAF post-deployment screening program of areas in which modifications would be beneficial. Collectively, the evaluation studies identified benefits to screening and found that it was mostly performing as expected. However, some unexpected findings highlight a need for improved communication on screening roles, responsibilities, and benefits, implementation of screening completion monitoring, augmented training for screening interviewers, and implementation of care provision monitoring when follow-up care is recommended. This screening program is currently undergoing modernization, and the evaluation studies can provide some guidance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.206 | 0.370 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".