Eating and Exercise‐Related Correlates of Weight Stigma: A Multinational Investigation
Bibliographic record
Abstract
OBJECTIVE: Weight stigma is prevalent across the world. However, little is known about whether and how the harmful health consequences of weight stigma may vary across countries. The current study examined the association between experiences of weight stigma and multiple eating and exercise-related indicators among a large, multinational sample of adults. METHODS: Adults enrolled in an international weight management program residing in Australia, Canada, France, Germany, the United Kingdom, and the United States completed identical Web-based surveys in the dominant language for their country. Participants (N = 13,996) reported on their personal experiences of weight stigma and health, including eating behaviors, attitudes toward exercise, and perceived stress. RESULTS: More than half of all participants in each country reported experiencing weight stigma. Participants who had experienced weight stigma reported engaging in more eating to cope, gym avoidance, and self-monitoring behaviors, as well as higher levels of stress and reduced eating self-efficacy. These associations were documented over and above sociodemographic characteristics and BMI and did not vary across countries. CONCLUSIONS: Study findings document uniform health-related correlates of weight stigma within a multinational context and underscore the need for global initiatives to curtail weight stigma in order to support population 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".