The impacts of gestational weight gain in Aboriginal populations in North America: A scoping review
Bibliographic record
Abstract
Background : Global gestational weight gain (GWG) is a major global issue, with rates increasing above predicted values (2). Previous research shows that excessive or inadequate weight gain during pregnancy results in adverse maternal and child outcomes (2). To date, a review on GWG in Aboriginal populations has not been conducted. To address the identified void, this scoping review will explore literature on the various outcomes associated with GWG in Aboriginal populations. Additionally, the beliefs and perspectives of Aborgines on GWG will be assessed. Methods : A scoping review framework was used to conduct database searches March 2015 in Embase, Medline, CINAHL, and Web of Science. The search resulted in 38 articles. Each article was reviewed based on specific inclusion criteria and 12 articles remained. Conclusions : It is hoped that this review will highlight the severity of Aboriginal GWG based on maternal and child outcomes and suggest a need for intervention. Aboriginal women with inadequate GWG are at a higher risk of gestational diabetes mellitus, preterm birth, are more likely to have children with Fetal Alcohol Syndrome or Fetal Alcohol Effect, have lower proportions of male infants, and have a reduced risk of preeclampsia. Aboriginal women with excessive GWG are at a higher risk of having perineal and vaginal trauma, infant macrosomia, preeclampsia, postpartum weight retention, obese children, and have a higher proportion of male infants.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".