Mental Illness–Related Stigma in Canadian Military and Civilian Populations: A Comparison Using Population Health Survey Data
Bibliographic record
Abstract
OBJECTIVE: This study sought to compare the prevalence and impacts of mental illness-related stigma among Canadian Armed Forces personnel and Canadian civilians. METHODS: Data were from two highly comparable, population-based, cross-sectional surveys of Canadian military personnel and Canadian civilians: the 2013 Canadian Forces Mental Health Survey (N=6,696) and the 2012 Canadian Community Health Survey-Mental Health (N=25,113), respectively. Perceived stigma was assessed among those who reported care seeking for a mental health problem in the past 12 months. Follow-up questions assessed the impact of stigma in various domains. Modified Poisson regression and linear regression were used to examine population differences (military versus civilian) in terms of care seeking, stigma, and stigma impact, with adjustments for sociodemographic characteristics and the need for care. RESULTS: Military personnel were significantly more likely than civilians to have perceived stigma (adjusted prevalence ratio [PR]=1.70, 95% confidence interval [CI]=1.11-2.60). Stigma had a greater impact on military personnel, particularly in terms of work or school life (b=1.01, CI=.57-1.47). However, military personnel were also significantly more likely than civilians to have sought care (PR=1.86, CI=1.53-2.25). CONCLUSIONS: Military personnel reported a disproportionate amount of mental illness-related stigma, compared with Canadian civilians, and a greater impact of stigma. Nevertheless, military personnel were more likely to seek care, pointing to a complex relationship between stigma and care seeking in the military.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".