Explicit and implicit weight bias towards preconception, pregnant, and postpartum higher-weight women: A six-country cross-sectional study of community members’ perspectives
Bibliographic record
Abstract
Background: No studies have evaluated weight bias towards preconception, pregnant, and postpartum (PPP) women across countries. We aimed to explore the explicit and implicit weight bias held by community members towards higher-weight PPP women across six countries. Methods: We conducted an anonymous, online, cross-sectional survey (May- July 2023). We recruited community-dwellers aged >18 years residing in Australia, Canada, the US, the UK, Malaysia, and India. Measures included explicit and implicit weight biases, causal beliefs, sociocultural awareness of appearance (societal norms), and demographic factors. Hierarchical multiple regression analyses examined associations between weight bias and associated factors. Results: Of the 514 respondents (mean age 49±18.2 years; range 18-84; 61.5% female), 60.1% were White. Residents of India (vs. other countries) reported lower explicit and implicit weight bias. Beliefs that weight is not solely under an individual’s control was associated significantly with less explicit (B= -0.02, p<0.001) and implicit (B= -0.11, p=0.01) bias. Societal norms were associated positively with explicit weight bias (B= 0.03, p<0.001). Conclusion: Weight bias is not limited to Western countries. Community members’ attitudes about the controllability of obesity and unhelpful social norms surrounding women's body size should be a key focus to reduce weight stigma against higher-weight PPP women.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".