Herbal medicines use in the Jordanian population: A nationally representative cross-sectional survey
Bibliographic record
Abstract
Abstract Context: Despite of potential adverse effects, the use of herbal medicines has grown globally without proper regulatory measures. There is a scarcity of data on the pattern of use and general awareness towards herbal products among the Jordanians. Aims: To assess the prevalence, utilization, and attitude toward herbal medicines among the Jordanian public. Methods: A national cross-sectional self-reported survey on a random sample of adult population aged ≥ 18 years was conducted over two months to include 1820 adults in Jordan. A representative sample was collected using a proportionate random sampling technique, which enabled us to categorize the study population geographically. SPSS V26 was used for data analysis. Results: The prevalence of herbal medicine use was 53.3% (971/1820), and respondents who aged >29 years were more likely to use herbal products. Predictors for using herbal products were: females (OR 4.23; 95%CI 1.97-9.55; p=0.0004), fair health status (OR 7.19; 95%CI 5.49-13.85; p=0.0001), and participants without chronic diseases were significantly less likely to use herbal medicines (OR 0.21; 95%CI 0.11-0.61; p=0.0007). The majority of respondents (86.5%, 1574/1820) thought herbal products were safe because they were made from natural ingredients. The most common reasons for using herbal products were chronic disease treatments (41.9%, 407/971), weight reduction (23.6%, 229/971), and to less extent improving the well-being (16.2%, 157/971). Conclusions: More than half of the targeted population used herbal medicines, a quarter of whom experienced adverse effects. The findings of this study have major community health implications for Jordan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".