Pairing Evidence-Based Strategies With Motivational Interviewing to Support Optimal Nutrition and Weight Gain in Pregnancy
Bibliographic record
Abstract
OBJECTIVE: Because eating, nutrition, and weight management patterns adopted during pregnancy may persist beyond the postpartum period, pregnancy provides an opportunity for health education that affects the future health of the pregnant person, the fetus, and the family. This systematic review aimed to find nutrition and weight management behaviors that could be used safely during pregnancy to optimize gestational weight gain. METHODS: PubMed, MEDLINE, and Web of Science were searched for research or systematic reviews published in English from 2018 to 2023 using terms including gestational weight gain maintenance, weight, management, pregnancy, behavior, strategy, and strategies. Excluded research used pediatric or adolescent populations, restrictive diets such as no carbohydrate or no fat diets, fasting, bariatric surgery, weight loss medications, private industry, or profit-earning programs using food brands or specific diet programs. RESULTS: The abstracts reviewed in these areas: excessive gestational weight gain (1019), low-glycemic index diet (640), Mediterranean diet (220), MyPlate diet (2), the Dietary Approaches to Stop Hypertension (DASH) diet (50), portion control (6), home meal preparation (6), mindful eating (13), intuitive eating (10), self-weighing (10), and motivational interviewing during pregnancy (107), were reduced to 102 studies. Studies in those 10 areas were reviewed for nutrition and eating behaviors that are safe to use during pregnancy and could be used along with motivational interviewing. CONCLUSION: Clinicians can discuss these behaviors using motivational interviewing techniques to assist clients in optimizing gestational weight gain. Dialogue examples pairing these strategies with motivational interviewing principles are included.
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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 | 0.020 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".