Use of complementary and alternative medicines by a sample of Australian women during pregnancy
Bibliographic record
Abstract
BACKGROUND: The use of complementary and alternative medicines (CAM) is growing in Australia, with women higher users than men. Yet, only a few Australian studies have explored the use of CAM during pregnancy. AIMS: To explore the use of CAM, the types of CAM practitioners consulted, physical symptoms/complaints for which CAM are used by a sample of pregnant Australian women, and women's perceptions of the efficacy of CAM in treating those complaints. METHODS: Three hundred and twenty-one pregnant women, who volunteered for a study exploring women's well-being during pregnancy, completed a self-report questionnaire in their late second/early third trimester. RESULTS: Seventy-three per cent of women had used at least one kind of complementary therapy in the prior eight weeks of pregnancy. Over one-third of the women had visited at least one alternative medicine practitioner during pregnancy. Approximately one-third of the women reported taking CAM to alleviate a specific physical symptom, with 95.7% of these women reporting they either got completely better or a little bit better with use of CAM; one quarter reported planning to use an alternative therapy to assist with labour preparation. Age, number of physical symptoms experienced, income level and level of education were not associated with greater use of CAM (P < 0.05); however, women reporting more physical symptoms were more likely to consult a CAM practitioner. CONCLUSION: Findings highlight the substantial use of CAM during pregnancy and the need to have all health professionals adequately informed about such therapies during this life stage.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".