Military Occupational Outcomes in Canadian Armed Forces Personnel with and without Deployment-Related Mental Disorders
Bibliographic record
Abstract
OBJECTIVE: Mental disorders are common in military organizations, and these frequently lead to functional impairments that can interfere with duties and lead to costly attrition. In Canada, the military mental health system has received heavy investment to improve occupational outcomes. We investigated military occupational outcomes of diagnosed mental disorders in a cohort of 30,513 personnel who deployed on the Afghanistan mission. METHODS: Cohort members were military personnel who deployed on the Afghanistan mission from 2001 to 2008. Mental disorder diagnoses and their attribution to the Afghanistan mission were ascertained via medical records in a stratified random sample (n = 2014). Career-limiting medical conditions (that is, condition-associated restrictions that reliably lead to medically related attrition) were determined using administrative data. Outcomes were assessed from first Afghanistan-related deployment return. RESULTS: At 5 years of follow-up, the Kaplan-Meier estimated cumulative fraction with career-limiting medical conditions was 40.9% (95% confidence interval [CI] 35.5 to 46.4) among individuals with Afghanistan service-related mental disorders (ARMD), 23.6% (CI 15.5 to 31.8) with other mental disorders, and 11.1% (CI 8.9 to 13.3) without mental disorders. The adjusted Cox regression hazard ratios for career-limiting medical condition risk were 4.89 (CI 3.85 to 6.23) among individuals with ARMD and 2.31 (CI 1.48 to 3.60) with other mental disorders, relative to those without mental disorders. CONCLUSIONS: Notwithstanding the Canadian military's mental health system investments, mental disorders (particularly ARMD) still led to a high risk of adverse military occupational outcomes. Such investments have intrinsic value but may not translate into reduced medically related attrition without improvements in prevention and treatment effectiveness.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".