Herbal medicine use in pregnancy: results of a multinational study
Bibliographic record
Abstract
BACKGROUND: The use of complementary and alternative medicines (CAM) is growing in the general population. Herbal medicines are used in all countries of the world and are included in the top CAM therapies used. METHODS: A multinational study on how women treat disease and pregnancy-related health ailments was conducted between October 2011 and February 2012 in Europe, North and South America and Australia. In this study, the primary aim was to determine the prevalence of herbal medicine use in pregnancy and factors related to such use across participating countries and regions. The secondary aim was to investigate who recommended the use of herbal medication in pregnancy. RESULTS: There were 9,459 women from 23 countries participating in the study. Of these, 28.9% reported the use of herbal medicines in pregnancy. Most herbal medicines were used for pregnancy-related health ailments such as cold and nausea. Ginger, cranberry, valerian and raspberry were the most commonly used herbs in pregnancy. The highest reported rate of herbal use medicines was in Russia (69%). Women from Eastern Europe (51.8%) and Australia (43.8%) were twice as likely to use an herbal medicine versus other regions. Women using herbal medicines were characteristically having their first child, non-smokers, using folic acid and consuming some alcohol in pregnancy. Also, women who were currently students and women with an education other than a high school degree were more likely to use herbal medicines than other women. Although 1 out of 5 women stated that a physician had recommended the herbal use, most women used herbal medicine in pregnancy on their own initiative. CONCLUSIONS: In this multinational study herbal medicine use in pregnancy was high although there were distinct differences in the herbs and users of herbal medicines across regions. Most commonly the women self-medicated with herbal medicine to treat pregnancy-related health ailments. More knowledge regarding the efficacy and safety of herbal medicines in pregnancy is warranted.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".