Prevalence and correlates of weight bias internalization in weight management: A multinational study
Bibliographic record
Abstract
Weight bias internalization (WBI) is an understudied form of internalized stigma, particularly among treatment-seeking adults with overweight/obesity. The current study surveyed 13,996 adults currently engaged in weight management in the first multinational study of WBI. From May to July 2020, participants in six Western countries completed the Modified Weight Bias Internalization Scale (WBIS-M) and measures of weight change, health behaviors, psychosocial well-being, and health-related quality of life (HRQOL). Participants were majority white, female, middle-aged, and categorized as having overweight or obesity based on body mass index. Results showed higher mean WBIS-M scores among participants in the UK, Australia, and France than in Germany, the US, and Canada. Across all countries, and controlling for participant characteristics and experiences of weight stigma, WBIS-M scores were associated with greater weight gain in the past year. Participants with higher WBIS-M scores also reported poorer mental and physical HRQOL, less eating and physical activity self-efficacy, greater engagement in eating as a coping strategy, more avoidance of going to the gym, poorer body image, and greater perceived stress. Few interaction effects were found between experiences and internalization of weight stigma. Overall, the current findings support WBI as a robust correlate of adverse weight-related health indices across six Western countries. Prospective and experimental studies are needed to determine directionality and causality in the relationship between WBI and poor health outcomes.
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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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".