Delay to mental healthcare in a cohort of Canadian Armed Forces personnel with deployment-related mental disorders, 2002–2011: a retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To assess whether the delay to care among Canadian Armed Forces (CAF) personnel who sought care for a mental disorder changed over time and in association with CAF mental health system augmentations. DESIGN: A stratified, random sample (n=2014) was selected for study from an Afghanistan-deployed cohort (N=30 513) and the 415 (weighted N=4108) individuals diagnosed with an Afghanistan service-related mental disorder were further assessed. Diagnosis-related data were abstracted from medical records (22 June 2010 to 30 May 2011). Other data were extracted from administrative databases. Delay to care was assessed across five mental health system eras: 2002/2004, 2005/2006, 2007, 2008 and 2009/2010. Weighted Cox proportional hazards regression assessed the association between era, handled as a time-dependent covariate, and the outcome while controlling for a broad range of potential confounders (ie, sociodemographic, military and clinical characteristics). Taylor series linearisation methods and sample design weights were applied in generating descriptive and regression analysis statistics. PRIMARY OUTCOME: The outcome was the delay to mental healthcare, defined as the latency from most recent Afghanistan deployment return date to diagnosis date, among individuals with an Afghanistan service-related mental disorder diagnosis. RESULTS: Mean delay to care was 551 days (95% CI 501 to 602); the median was 400 days. Delay to care decreased in subsequent eras relative to 2002/2004; however, only the most recent era (2009/2010) was statistically significant (adjusted HR (aHR): 3.01 (95% CI 1.91 to 4.73)). Men, operations support occupations, higher ranks, non-musculoskeletal comorbidities and fewer years of military service were also independently associated with longer delays to care. CONCLUSIONS: CAF mental health system changes were associated with reduced delays to mental healthcare. Further evaluation research is needed to identify the key system changes that were most impactful.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".