A population survey on the use of 24 common medicinal herbs in Australia
Bibliographic record
Abstract
BACKGROUND: Herbal medicine use is common in Australia but little is known about the use of individual herbs. METHODS: A cross-sectional population survey conducted in 2007 with a sample of 2526, in the Australian state of Victoria. RESULTS: Almost a quarter (22.6%, 95% confidence interval (CI): 20.9-24.2%) of survey participants had used at least one medicinal herb in the preceding 12 months. Aloe vera, garlic and green tea were the most popular, each used by about 10% of participants. Health enhancement was the most common reason for herbal medicine use (69.6% of users) but relatively high proportions of users sought relief of specific medical conditions. Over 90% considered their herbal medicine to be very or somewhat helpful. Less than half (46.6%) the users were aware that there were potential risks associated with herbal medicine. Relatively high proportions of female users had taken herbal medicine whilst pregnant (14.4%) and/or whilst breast feeding (10.0%). Over half (50.9%) of herbal medicine users had also used Western medicine for the same medical condition in the 12-month period. Almost the same proportion (49.9%) had used both forms of medication on the same day. In deciding whether or not to use herbal medicine, the vast majority of survey participants indicated that they would accept the advice of their medical practitioner. CONCLUSIONS: In addition to health enhancement, specific herbs are commonly used to treat a range of medical conditions, without clear evidence of efficacy. Concurrent use of herbal and conventional medicine is relatively common and the majority of herbal medicine users are not aware of potential adverse effects. It appears that medical practitioners could exert significant influence on their patients' decisions about herbal medicine 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".