The WOMBS Framework: A review and new theoretical model for investigating pregnancy‐related weight stigma and its intergenerational implications
Bibliographic record
Abstract
As the growing weight stigma literature has developed, one critically relevant and vulnerable population has received little consideration-pregnant and postpartum women. Because weight fluctuations are inherent to this life phase, and rates of prepregnancy overweight and obesity are already high, this gap is problematic. More recently, however, there has been a rising interest in pregnancy-related weight stigma and its consequences. This paper therefore sought to (a) review the emerging research on pregnancy-related weight stigma phenomenology and (b) integrate this existing evidence to present a novel theoretical framework for studying pregnancy-related weight stigma. The Weight gain, Obesity, Maternal-child Biobehavioral pathways, and Stigma (WOMBS) Framework proposes psychophysiological mechanisms linking pregnancy-related weight stigmatization to increased risk of weight gain and, in turn, downstream childhood obesity risk. This WOMBS Framework highlights pregnant and postpartum women as a theoretically unique at-risk population for whom this social stigma engages maternal physiology and transfers obesity risk to the child via social and physiological mechanisms. The WOMBS Framework provides a novel and useful tool to guide the emerging pregnancy-related weight stigma research and, ultimately, support stigma-reduction efforts in this critical context.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".