Inconsistent Weight Communication Among Prenatal Healthcare Providers and Patients: A Narrative Review
Bibliographic record
Abstract
Importance Gestational weight gain (GWG) is an independent and modifiable factor for a healthy pregnancy. Gestational weight gain above or below the Institute of Medicine Guidelines has been shown to impact both maternal and fetal health (eg, gestational diabetes, hypertension, downstream obesity). Healthcare providers (HCPs) have the potential to be reliable sources of evidence-based weight information and advice during pregnancy. Objective The aim of this study was to summarize the literature assessing GWG discussions between patients and their HCPs in a clinical setting to better understand the knowledge that is currently being exchanged. Evidence Acquisition A literature review was conducted by searching Ovid Medline, CINAHL, and Embase databases. All relevant primary research articles in English that assessed GWG discussions were included, whereas intervention studies were excluded. Results A total of 54 articles were included in this review. Although the overall prevalence and content of GWG counseling varied between studies, counseling was often infrequent and inaccurate. Healthcare providers tended to focus more on women experiencing obesity and excessive GWG, as opposed to the other body mass index categories or inadequate GWG. Women of higher socioeconomic status, older age, nulliparous, history of dieting, low physical activity, and those categorized as overweight/obese were more likely to receive GWG advice. Patients also reported receiving conflicting facts between different HCP disciplines. Conclusions The evidence regarding GWG counseling in prenatal care remains variable, with discrepancies between geographic regions, patient populations, and HCP disciplines. Relevance Healthcare providers should counsel their pregnant patients on GWG with advice that is concordant with the Institute of Medicine Guidelines. Target Audience Obstetricians and gynecologists, family physicians, midwives, and prenatal healthcare providers. Learning Objectives After completing this activity, the learner should be better able to critique the gaps in prenatal health education regarding GWG, assess the impact that various HCPs have on a patient's weight gain practices, and distinguish factors that contribute to useful and helpful GWG counseling.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".