Risk Screening of Veterans Throughout the Life Course
Bibliographic record
Abstract
Both the Canadian Armed Forces and Veterans Affairs Canada identified the need for a brief standardized tool to screen military members and veterans for the risk of a difficult adjustment to civilian life, frailty, suicide and homelessness. Data from Life After Service Studies (n = 8,101) were used to build logistic regression models of difficult adjustment to civilian life. The resulting brief risk screener was piloted in 2018 (n = 246). The modeling considered 28 risk indicators, used 17 of these to build the models, and maintained 8 questions for a brief risk screener. Optimal cutoff was found with a threshold of 3+ for difficult adjustment to civilian life, with 39% sensitivity (95% CI: 37.9 to 41.1) and 94% specificity (95% CI: 93.1 to 94.6). A longer 10 item questionnaire was implemented. Pilot participants who were help-seeking veteran clients had frequency by risk level of 42% low, 40% moderate, and 18% high. Pilot participants who were serving military members had frequency by risk level of 79% low, 13% moderate, and 8% high. In 2019, Canadian government implemented a new standardized risk screening tool to improve the effectiveness of services and referrals.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".