Nausea and vomiting in pregnancy: results of a survey that identified interventions used by women to alleviate their symptoms
Bibliographic record
Abstract
Nausea and vomiting of pregnancy (NVP) affects most pregnant women. There are safe and effective treatments available; however, most women choose to avoid pharmacological therapies and try lifestyle and dietary changes to treat their condition. To date, no attempt has been made to quantify women's experience with a variety of interventions. This study aims to identify factors commonly reported by women that alleviate their symptoms of NVP. Five hundred women with NVP, calling a pregnancy healthline between February 1996 and July 1999, completed a questionnaire where they were asked to rate which of 21 factors helped and to what extent each factor helped to improve their NVP symptoms. For each item, the 'frequency' (percentage of women who indicated that item as an improvement) and 'mean importance' (mean importance score of women who indicated that item as an improvement) were multiplied to give the 'overall impact' score. All 500 women reported that dietary and lifestyle changes helped to improve their NVP symptoms. However, most items were rated low and only 31% of women reported benefit from the use of pharmacological treatment. In conclusion, this study has identified that NVP is a multifaceted condition. Lifestyle changes including validation, supportive counseling and dietary adjustments are important components, that can be used to counsel women with NVP, concomitantly with safe and effective treatment.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".