Recommendations for follow-up care during post-deployment screening of Canadian Armed Forces personnel: how well does self-reported mental health predict referral decisions?
Bibliographic record
Abstract
OBJECTIVE: Canadian Armed Forces (CAF) post-deployment screening aims to facilitate early care for members with mental health issues. The process consists of a questionnaire to screen for mental health problems, followed by an interview with a healthcare provider during which recommendations for follow-up care are provided if needed. In this study, we examined the association of self-reported mental health from the screening questionnaire with recommendation for follow-up care during the interview. DESIGN: Using screening data collected from CAF members who deployed from 2009 to 2012 (n=14 957), logistic regression analysis was conducted to examine the association of self-reported mental health from the screening questionnaire with clinicians' recommendation for follow-up care. RESULTS: In total, 19.7% of screened individuals were recommended for follow-up care. In the adjusted logistic regression model, some demographic characteristics, as well current and prior mental healthcare and self-reported mental health problems, had a substantial association with recommendation for follow-up. Compared with each mental health problem's lowest severity category, recommendation for follow-up care was higher by approximately 12%-17% for those with mild to severe depression, 7% for those with panic disorder, 8%-10% for those with mild to severe anxiety, 8% for those experiencing high levels of stressors, 4%-10% for those at risk of alcohol use disorder and 7%-12% for those at risk of post-traumatic stress disorder. CONCLUSIONS: Although the presence of mental health problems was significantly associated with receiving a follow-up recommendation, the relationships between self-reported mental health and subsequent recommendations for care were not as high as expected. Although this may partly reflect time delays between the questionnaire and interview, further research is needed on the extent to which other factors contributed to referral decisions.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".