Structural Vulnerability Factors and Gestational Weight Gain: A Scoping Review on the Extent, Range, and Nature of the Literature
Bibliographic record
Abstract
Background: Inadequate and excessive gestational weight gain (GWG) are rising epidemiological health concerns, affecting a substantial proportion of pregnant women in high-income countries and contributing to a multitude of adverse maternal and infant health outcomes. The aim of this scoping review was to identify key structural vulnerability factors (SVFs) related to GWG, and to examine the extent, range, and nature of the existing literature to inform future research. Methods: Electronic searches were performed in October 2018 (updated in August 2019) in MEDLINE(R) ALL, EMBASE, PsycINFO, CINAHL, and Sociological Abstracts databases. Eligible studies had an observational design, had to be conducted before COVID-19, in a high-income country, have pregnant participants, and perform inferential statistics between an SVF and GWG. Results: Of the 157 included articles, the eight SVFs most commonly studied in association with GWG were race/ethnicity (n=91 articles), age (n=87), parity (n=48), education (n=44), income (n=39), marital status (n=28), immigration (n=19), and abuse (n=12). Substantial heterogeneity across study contexts, methodologies, populations, and findings was identified. Studies spanned 22 high-income countries, were predominantly conducted in the USA (77%), and most studies (60%) had a retrospective design. Race/ethnicity was the most extensively studied factor, covering the longest time period (since 1976) and having the largest sample size, and the second-highest proportion of studies reporting a significant relationship with GWG (79%), following immigration status (95%). Conclusions: Given the heterogeneity in findings across studies, adopting an intersectional approach may enhance our understanding of the complex interplay between SVFs and the social context in relation to GWG. This nuanced perspective is critical for informing future research and developing effective strategies to address the pervasive perinatal health challenges associated with inadequate and excessive GWG.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| 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".