Preventing excessive gestational weight gain in obese pregnant women: Does adherence to nutrition and exercise behaviour change programs improve with adding a family based component?
Bibliographic record
Abstract
Introduction: Excessive gestational weight gain (EGWG) can be prevented by a structured nutrition and exercise behaviour change program implemented during pregnancy, however research is inconsistent on the efficacy of these programs in the obese (body mass index; BMI≥30.0kg·m2) pregnant population. The purpose of this study was to determine if there was an increase in adherence to nutrition and exercise goals from obese pregnant women if a family based component was added to the intervention. Methods: Retrospectively, obese pregnant women who participated in the (NELIP; a nutrition and exercise program designed to prevent EGWG, n=38) and the Family-based Behavioural Treatment (FBBT) plus NELIP (FBBT+NELIP; n=29) were scored on meeting the goals of the program (3 exercise and 3 nutrition) for a maximum adherence score of 6. Weight gain on the program was assessed as gained appropriately or excessively according to the Institution of Medicine (2009) guidelines. Adherence scores were compared between women who gained excessively or appropriately within and across programs. Results: Within programs women who gained appropriately had significantly greater overall adherence than women who gained excessively (p
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".