Investigating the normalization and normative views of gestational weight gain: Balancing recommendations with the promotion and support of healthy pregnancy diets
Bibliographic record
Abstract
Abstract Objectives Gestational weight gain (GWG) is increasingly monitored in the United States and Canada. While promoting healthy GWG offers benefits, there may be costs with over‐surveillance. We aimed to explore these costs/benefits. Methods Quantitative data from 350 pregnant survey respondents and qualitative focus group data from 43 pregnant/post‐partum and care‐provider participants were collected in the Mothers to Babies (M2B) study in Hamilton, Canada. We report descriptive statistics and discussion themes on GWG trajectories, advice, knowledge, perceptions, and pregnancy diet. Relationships between GWG monitoring/normalization and worry, knowledge, diet quality, and sociodemographics—namely low‐income and racialization—were assessed using χ 2 tests and a linear regression model and contextualized with focus group data. Results Most survey respondents reported GWG outside recommended ranges but rejected the mid‐20th century cultural norm of “eating for two”; many worried about gaining excessively. Conversely, respondents living in very low‐income households were more likely to be gaining less than recommended GWG and to worry about gaining too little. A majority had received advice about GWG, yet half were unable to identify the range recommended for their prepregnancy BMI. This proportion was even lower for racialized respondents. Pregnancy diet quality was associated with household income, but not with receipt or understanding of GWG guidance. Care‐providers encouraged normalized GWG, while worrying about the consequences of pathologizing “abnormal” GWG. Conclusions Translation of GWG recommendations should be done with a critical understanding of GWG biological normalcy. Supportive GWG monitoring and counseling should consider clinical, socioeconomic, and community contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".