Addressing cultural, racial and ethnic discrepancies in guideline discordant gestational weight gain: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To systematically review the literature and describe the discrepancies in achieving the 2009 Institute of Medicine (IOM) gestational weight gain (GWG) guidelines across cultures. METHODS: Ten databases were searched from inception to April 2018. Observational cohort studies were included that examined adult women; reported on a measure of culture; compared cultural groups, and reported on GWG. Articles were broken down into papers that used the current 2009 IOM GWG guidelines and those that used others. A meta-analysis was conducted for studies using the 2009 guidelines examining the prevalence of discordant GWG across cultural groups. RESULTS: The review included 86 studies. Overall, 69% of women experienced discordant GWG irrespective of culture. White women experienced excessive GWG most often, and significantly more than Asian and Hispanic women; Black women had a higher prevalence of excessive GWG than Hispanic and Asian women; however, this difference was not significant. CONCLUSIONS: The majority of women experience excessive GWG, with White women experiencing this most often. Culturally diverse GWG guidelines are needed to individualize antenatal care and promote optimal maternal-fetal health outcomes across cultural groups.
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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 | 0.019 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".