The roles of experienced and internalized weight stigma in healthcare experiences: Perspectives of adults engaged in weight management across six countries
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Considerable evidence from U.S. studies suggests that weight stigma is consequential for patient-provider interactions and healthcare for people with high body weight. Despite international calls for efforts to reduce weight stigma in the medical community, cross-country research is lacking in this field. This study provides the first multinational investigation of associations between weight stigma and healthcare experiences across six Western countries. METHODS: Participants were 13,996 adults residing in Australia, Canada, France, Germany, the UK, and the US who were actively enrolled in an internationally available behavioral weight management program. Participants completed identical online surveys in the dominant language for their country that assessed experienced weight stigma, internalized weight bias, and healthcare behaviors and experiences including perceived quality of care, avoidance or delay of seeking care, experiences with providers, and perceived weight stigma from doctors. RESULTS: Among participants who reported a history of weight stigma (56-61%), two-thirds of participants in each country reported experiencing weight stigma from doctors. Across all six countries, after accounting for demographics, BMI, and experienced stigma, participants with higher internalized weight bias reported greater healthcare avoidance, increased perceived judgment from doctors due to body weight, lower frequency of obtaining routine checkups, less frequent listening and respect from providers, and lower quality of healthcare. Additionally, experienced weight stigma (from any source) was indirectly associated with poorer healthcare experiences through weight bias internalization, consistently across the six countries. CONCLUSIONS: Weight stigma in healthcare is prevalent among adults actively engaged in weight management across different Western countries, and internalized weight bias has negative implications for healthcare even after controlling for BMI. The similar findings across all six countries underscore the negative consequences of weight stigma on healthcare behaviors and experiences, and emphasize the need for collective international efforts to address this problem.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".