The use of CAM by women suffering from nausea and vomiting during pregnancy
Bibliographic record
Abstract
BACKGROUND: Nausea and vomiting during pregnancy (NVP) affects two-thirds of pregnant women to varying degrees and over the years many modalities have been used to try to alleviate this often debilitating condition. There is a paucity of information in the literature about the use or efficacy of complementary and alternative medicine (CAM) for the treatment of this condition that affects so many women. Our primary objective was to examine the prevalence of CAM usage by women suffering from NVP. Our secondary objective was to ascertain if women had any supervision in the use of these treatments. METHODS: Women who called The Motherisk NVP helpline, were asked after the counseling session to complete a questionnaire, which included demographic data as well as information about their CAM use. RESULTS: Seventy women completed the questionnaire. 61% reported using CAM therapies, of which the three most popular were: ginger, vitamin B6 and acupressure. 21% of those who reported using CAM, had consulted CAM practitioners, 8% their physicians or pharmacists and 71% discussed the usage with family, friends and other allied health professionals. Women who did not use CAM stated they would probably use these modalities if there was more information about the safety in pregnancy. CONCLUSION: Pregnant women with NVP are mirroring the trend in the general population of the use of CAM. They are also using CAM therapies with little supervision from practitioners experienced in the use of these modalities.
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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 | 0.001 |
| 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) | 0.002 | 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".