Pregnant women's perceptions of gestational weight gain: A systematic review and meta‐synthesis of qualitative research
Bibliographic record
Abstract
Excess gestational weight gain has numerous negative health outcomes for women and children, including high blood pressure, diabetes, and cesarean section (maternal) and high birth weight, trauma at birth, and asphyxia (infants). Excess weight gain in pregnancy is associated with a higher risk of long-term obesity in both mothers and children. Despite a concerted public health effort, the proportion of pregnant women gaining weight in excess of national guidelines continues to increase. To understand this phenomenon and offer suggestions for improving interventions, we conducted a systematic review of qualitative research on pregnant women's perceptions and experiences of weight gain in pregnancy. We used the methodology of qualitative meta-synthesis to analyze 42 empirical qualitative research studies conducted in high-income countries and published between 2005 and 2015. With this synthesis, we provide an account of the underlying factors and circumstances (barriers, facilitators, and motivators) that pregnant women identify as important for appropriate weight gain. We also offer a description of the strategies identified by pregnant women as acceptable and appropriate ways to promote healthy weight gain. Through our integrative analysis, we identify women's common perception on the struggle to enact health behaviors and physical, social, and environmental factors outside of their control. Effective and sensitive interventions to encourage healthy weight gain in pregnancy must consider the social environment in which decisions about weight take place.
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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 | 0.037 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".