Policies to address weight discrimination and bullying: Perspectives of adults engaged in weight management from six nations
Bibliographic record
Abstract
OBJECTIVE: Across the world, it remains legal to discriminate against people because of their weight. Although US studies demonstrate public support for laws to prohibit weight discrimination, multinational research is scarce. The present study conducted a multinational comparison of support for legislative measures to address weight discrimination and bullying across six countries. METHODS: Participants were adults (n = 13,996) enrolled in an international weight-management program and residing in Australia, Canada, France, Germany, the UK, and the US. Participants completed identical online surveys that assessed support for antidiscrimination laws and policies to address weight bullying, demographic characteristics, and personal experiences of weight stigma. RESULTS: Across countries, support was high for laws (90%) and policies (92%) to address weight-based bullying, whereas greater between-country variation emerged in support for legislation to address weight-based discrimination in employment (61%, 79%), as a human rights issue (57%), and through existing disability protections (47%). Findings highlight few and inconsistent links between policy support and sociodemographic correlates or experienced or internalized weight stigma. CONCLUSIONS: Support for policies to address weight stigma is present among people engaged in weight management across Westernized countries; findings offer an informative comparison point for future cross-country research and can inform policy discourse to address weight discrimination and bullying.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".