Stigma doesn’t discriminate: physical and mental health and stigma in Canadian military personnel and Canadian civilians
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it