Exploring weight bias internalization in pregnancy
Bibliographic record
Abstract
Abstract Background Recent research has shown that pregnant individuals experience weight stigma throughout gestation, including negative comments and judgement associated with gestational weight gain (GWG). Weight bias internalization (WBI) is often a result of exposure to weight stigma and is detrimental to biopsychological health outcomes. The purpose of this study was to explore WBI in pregnancy and compare scores based on maternal weight-related factors including pre-pregnancy body mass index (BMI), obesity diagnosis and excessive GWG. Methods Pregnant individuals in Canada and USA completed a modified version of the Adult Weight Bias Internalization Scale. Self-reported pre-pregnancy height and weight were collected to calculate and classify pre-pregnancy BMI. Current weight was also reported to calculate GWG, which was then classified as excessive or not based on Institute of Medicine (2009) guidelines. Participants indicated if they were diagnosed with obesity by a healthcare provider. Inferential analyses were performed comparing WBI scores according to pre-pregnancy BMI, excessive GWG, and obesity diagnosis. Significance was accepted as p < 0.05 and effect sizes accompanied all analyses. Result 336 pregnant individuals completed the survey, with an average WBI score of 3.9 ± 1.2. WBI was higher among those who had a pre-pregnancy BMI of obese than normal weight (p = 0.04, η2 = 0.03), diagnosed with obesity than not diagnosed (p < 0.001, Cohen’s d = 1.3), and gained excessively versus not (p < 0.001, Cohen’s d = 1.2). Conclusions Pregnant individuals who have a higher BMI, obesity and gain excessively may experience WBI. Given that weight stigma frequently occurs in pregnancy, effective person-oriented strategies are needed to mitigate stigma and prevent and care for WBI.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".