Deployment-related mental disorders among Canadian Forces personnel deployed in support of the mission in Afghanistan, 2001–2008
Bibliographic record
Abstract
BACKGROUND: The conflict in Afghanistan has exposed more Canadian Forces personnel to a greater degree of adversity than at any time in recent memory. We determined the incidence of Afghanistan deployment-related mental disorders and associated risk factors among personnel previously deployed in support of this mission. METHODS: The study population consisted of 30,513 Canadian Forces personnel who began a deployment in support of the mission in Afghanistan before Jan. 1, 2009. The primary outcome was a mental disorder perceived by a Canadian Forces clinician to be related to the Afghanistan deployment. Data on diagnoses and perceptions were abstracted from medical records of a stratified random sample of 2014 personnel. Sample design weights were used in all analyses to generate descriptive statistics for the entire study population. RESULTS: Over a median follow-up of 1364 days, 13.5% (95% confidence interval [CI] 12.1%-14.8%) of the study population had a mental disorder that was attributed to the Afghanistan deployment. Posttraumatic stress disorder was the most common diagnosis (in 8.0%, 95% CI 7.0%-9.0%, of personnel). Deployment to higher-threat locations, service in the Canadian Army and lower rank were independent risk factors associated with an Afghanistan-related diagnosis (e.g., hazard ratio for deployment to Kandahar Province 5.6, 95% CI 2.6-12.5, relative to deployment to the United Arab Emirates). In contrast, sex, Reserve Forces status, multiple deployments and deployment length were not independent risk factors. INTERPRETATION: An important minority of Canadian Forces personnel deployed in support of the Afghanistan mission had a diagnosis of a mental disorder perceived to be related to the deployment. Determining long-term outcomes is an important next step.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".