Presentation of a weight bias internalization tool for use in pregnancy and a call for future research: A commentary
Bibliographic record
Abstract
Background: Emerging evidence has shown that weight stigma is a concern during pregnancy, with several studies documenting common sources including healthcare, the media and interpersonal networks. Experiencing weight stigma may lead to weight bias internalization (WBI), whereby individuals accept and self-direct negative weight-related stereotypes, and limited research has assessed this in the context of pregnancy. Pregnancy is unique in terms of weight changes as many individuals will experience gestational weight gain (GWG). Accordingly, a WBI tool that accounts for GWG may be a more population-specific resource to use. Methods: This commentary presents a pregnancy-specific WBI tool that accounts for GWG. The validated Adult WBI scale was modified to include 'pregnancy weight gain'. This commentary also presents a brief summary of research that has assessed WBI in pregnancy and recommendations for future work. Results: Recommended future work includes validation of the pregnancy-specific WBI tool and prospective examinations of weight stigma and WBI in pregnancy and implications on maternal and newborn outcomes. Conclusion: Ultimately this research may inform development of interventions and resources to mitigate weight stigma and WBI in pregnancy and overall may contribute to improving prenatal outcomes and experiences.
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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 | 0.072 | 0.330 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.042 | 0.042 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".