Weight Stigma in Latin America, Asia, the Middle East, and Africa: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: Being stigmatized because of one's weight can pose physical, mental, and social challenges. While weight stigma and its consequences are established throughout Europe, North America, and Australasia, less is known about weight stigma in other regions. The objective of this study was to identify the extent and focus of weight stigma research in Latin America, Asia, the Middle East, and Africa. METHODS: A scoping review of weight stigma research in Latin America, Asia, the Middle East, and Africa was conducted. SCOPUS and PsychINFO databases were searched, and weight stigma experts were contacted to identify relevant literature. Sources were classified based on country/region, population, setting, and category of weight stigma researched. RESULTS: A total of 130 sources were identified from 33 countries and territories. Results indicate that weight stigma has been investigated across populations and settings, mainly focusing on manifestations of weight stigma through experiences, practices, drivers, and personal outcomes of these manifestations. CONCLUSIONS: Weight stigma is a developing global health concern not restricted to Europe, North America, and Australasia. The extent and focus of weight stigma research in Latin America, Asia, the Middle East, and Africa vary between countries and regions leaving several research gaps that require further investigation.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".