Predictors of Prenatal Multivitamin Adherence in Pregnant Women
Bibliographic record
Abstract
There is no study available that has investigated determinants of prenatal multivitamin adherence among pregnant women, based on gastrointestinal (GI) adverse events. The objective of this study was to identify determinants predicting adherence to prenatal multivitamins in pregnant women who were randomized to take 2 different supplements. The authors recruited and interviewed 70 women on the importance of various factors that may have affected adherence to previous and assigned multivitamins. The different factors included GI symptoms and swallowing difficulty. The authors used a 5-point scale to measure degree of importance. The highest scoring factors for not taking or discontinuing any previous multivitamins were fear of or experience of nausea, vomiting, and gagging. For women who never took the assigned prenatal multivitamins, the highest scoring factors contributing to that decision were fear of nausea, fear of vomiting, and health care provider advice. For women who started taking the assigned supplements, the most important factors affecting adherence were dosing regimen, health care provider advice, and mode of product distribution. Adherence to assigned prenatal multivitamins significantly correlated only with the importance of constipation in deciding to discontinue any previous multivitamins. It is concluded that predictors of adherence to recommended prenatal multivitamins during pregnancy are rooted in women's prior experiences with multivitamin use.
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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 | 0.008 |
| 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 | 0.001 |
| 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".