Stigma doesn’t discriminate: physical and mental health and stigma in Canadian military personnel and Canadian civilians
Bibliographic record
Abstract
BACKGROUND: Illness-related stigma has been identified as an important public health concern. Past research suggests there is a disproportionate risk of mental-health stigma in the military, but this same finding has not yet been established for physical-health stigma. The current study aimed to assess the independent contribution of mental and physical health on both enacted stigma (discriminatory behaviour) and felt stigma (feelings of embarrassment) and to determine whether these associations were stronger for military personnel than civilians. METHODS: Data were obtained from the 2002 Canadian Community Health Survey - Mental Health and Well-being and its corresponding Canadian Forces Supplement. Logistic regressions were used to examine a potential interaction between population (military [N = 1900] versus civilian [N = 2960]), mental health, and physical health in predicting both enacted and felt stigma, with adjustments made for socio-demographic information, mental health characteristics, and disability. RESULTS: Mental health did not predict enacted or felt stigma as a main effect nor in an interaction. There was a strong link between physical health and enacted and felt stigma, where worse physical health was associated with an increased likelihood of experiencing both facets of stigma. The link between physical health and enacted stigma was significantly stronger for military personnel than for civilians. CONCLUSIONS: Physical health stigma appears to be present for both civilians and military personnel, but more so for military personnel. Elements of military culture (e.g., the way care is sought, culture of toughness, strict fitness requirements) as well as the physical demands of the job could be potential predictors of group differences.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".