Does screening shorten delays to care for post-deployment mental disorders in military personnel? A longitudinal retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To determine whether post-deployment screening is associated with a shorter delay to diagnosis and care among individuals identified with a deployment-related mental disorder. DESIGN: Retrospective cohort study. SETTING: Canadian military population. PARTICIPANTS: The cohort consisted of personnel (n=28 460) with a deployment within the 2009 to 2014 time frame. A stratified random sample (n=3004) was selected for medical chart review. We restricted our analysis to individuals who had an opportunity to undergo screening and were subsequently diagnosed with a mental disorder that a clinician indicated was deployment-related (n=1157). INTERVENTIONS: Post-deployment health screening. MAIN OUTCOME MEASURE: The outcome was delay to diagnosis and care, the latency from individuals' deployment return to their mental disorder diagnosis date. Cox proportional hazards regression assessed screening's influence on this outcome. RESULTS: 74.4% of the study population had screened. Overall, the median delay to care was 766 days, 578 days among screeners and 928 days among non-screeners-a 350-day difference. Cox regression indicated that screeners had a significantly shorter delay to care (adjusted HR (aHR), 1.43 (95% CI, 1.11 to 1.86)). Screening findings had a substantial influence on delay to care. Identification of a mental health concern, whether a 'major' concern (aHR, 3.36 (95% CI, 2.38 to 4.73)) or a 'minor' concern (aHR, 1.46 (95% CI, 1.08 to 1.99)), and a recommendation for mental health services follow-up (aHR, 2.35 (95% CI, 1.73 to 3.21)) were strongly associated with shorter delays to care relative to non-screeners. CONCLUSIONS: Reduced delays to care are anticipated to lead to beneficial outcomes for both the individual and military organisation. We found that screening was associated with a shortened delay to care for mental disorders that were deployment-related. Future work will further explore this screening's components and optimisation strategies.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".