Habitual Use of Medicinal Plants among a Group of Jordanian Elderly According to Physical Activity and Gender
Bibliographic record
Abstract
Introduction: Traditional medicine use has grown significantly in the last few decades around the world. Similarly, in Jordan. The information regarding the percentages of older adults in Jordan who adopt a healthy life-style and consuming medicinal plants along with performing physical activity are limited.Objective: To evaluates the use of medicinal plants among a sample of Jordanian elderly population and the effect of physical activity and gender on their habitual medicinal plants usage.Method: A cross sectional study was conducted on 120 elderly Jordanian (62 women; 58 men) and evaluated for medicinal plant usage. A questionnaire was used for collecting personal, social, anthropometries and lifestyle information including the daily activities through a personal interview by the principal investigator.Results: In this study about 90% of all participates were using medicinal plants and 95% of males and females used medicinal plants were physically active. Females were used medicinal plants (100%) more than male (79.31%), the most medicinal plants used among elderlies were sage (88.33%), thyme (85.00%) and peppermint (81.67%).Conclusion: The study highlight the increasing number of herbal users among Jordanian elderlies especially females and physically active people and alarming about the possible risk associated with herbal/drug interactions among this age group.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".