Who drives weight stigma? A multinational exploration of clustering characteristics behind weight bias against preconception, pregnant, and postpartum women
Bibliographic record
Abstract
BACKGROUND: Weight bias is a global health challenge and community members are endorsed as the most common source of weight bias. The nature of weight biases specifically against preconception, pregnant, and postpartum (PPP) women from the perspective of community members is not known, especially in terms of cross-cultural trends. We investigated the magnitude of explicit and implicit weight bias and profiles of characteristics associated with harbouring weight bias. METHODS: We conducted a multinational investigation of clusters of factors associated with weight bias against PPP women (May-July 2023). Community members from Australia, Canada, United States (US), United Kingdom (UK), Malaysia, and India completed a cross-sectional survey measuring explicit and implicit weight biases, beliefs about weight controllability, and awareness of sociocultural body ideals. Hierarchical multiple regression and latent profile analyses identified clusters of factors associated with weight bias. RESULTS: Participants from India reported the lowest explicit weight bias (B = -0.45, p = 0.02). Participants from Australia (B = -0.14, p = 0.04) and the UK (B = -0.16, p = 0.02) (vs. US) reported the lowest implicit weight bias. Three distinct profiles were identified clustering on body mass index (BMI) and weight-controllability beliefs: low-BMI/moderate-beliefs, high-BMI/more biased beliefs, and high-BMI/less biased beliefs. Profile membership varied by country of residence and weight bias outcomes with low-BMI/moderate-beliefs profiles containing more people from non-Western countries and with low explicit weight bias. CONCLUSIONS: Explicit and implicit weight bias was harboured by participants across all included nations, although less pronounced in non-Western countries. Our profiles highlight that individuals who held a stronger belief that weight is controllable, regardless of their body weight, should be targeted for interventions to eliminate weight stigma.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".