Weight-based discrimination and the intersection with other forms of discrimination on self-report health outcomes among Canadians: A population-based cross-sectional study
Bibliographic record
Abstract
Little is known of the association between weight-based discrimination (WBD), and the intersection with other experiences of discrimination, and health outcomes. We investigated the association between WBD only, WBD and other forms of discrimination, other only, compared to no discrimination, and presence of self-reported unmet healthcare needs, satisfaction with healthcare, and general health. We conducted a cross-sectional study using data from the 2013 Canadian Community Health Survey (n=18,979) among a representative sample of Canadians aged 12 and up. Odds ratios (OR) were estimated from logistic regression adjusting for age, sex, and BMI. Results were also stratified by sex and BMI. Compared to no discrimination, people who experienced WBD only (OR=1.72, 95% CI=1.03-2.88), WBD and other discrimination (OR=2.71, 95% CI=1.76-4.18), and other discrimination only (OR=2.29, 95% CI=1.98-2.66) all had increased unmet healthcare needs. Similar, strong statistically significant associations were observed for dissatisfaction with healthcare, and poor general health. These associations were stronger among women and higher weight individuals. Our results suggest that experiences of WBD—alone and with other forms of discrimination—are associated with poor self-reported health outcomes. There is a need to address the impact of weight and other forms of stigma on healthcare access, quality, and general health.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".