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Habitual Use of Medicinal Plants among a Group of Jordanian Elderly According to Physical Activity and Gender

2019· article· en· W4213267271 on OpenAlexvenueno aff
Safaa A. Al-Zeidaneen, Hadil S. Subih, Ala'a Al‐Bakheit, Nahla Al-Bayyari, Seham M. Abu Jadayil

Bibliographic record

VenueJournal of Pharmacy and Nutrition Sciences · 2019
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinal plantsTraditional medicineLife styleMedicinePopulationGerontologyEnvironmental health

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.207

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.105
GPT teacher head0.424
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

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