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Record W1978581461 · doi:10.5539/gjhs.v7n1p173

Attitudes and Practices of Complementary and Alternative Medicine Among Adolescents in Saudi Arabia

2014· article· en· W1978581461 on OpenAlexvenueno aff
Abdulrahman O. Musaiger, Nada A. Abahussain

Bibliographic record

VenueGlobal Journal of Health Science · 2014
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAlternative medicineAcupunctureFamily medicineTraditional medicineStratified sampling

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the attitudes and use of complementary and alternative medicine (CAM) among Saudi Arabian adolescents. A multistage stratified sampling method was used to select 736 adolescents (358 males, 378 females) aged 15-19 years from secondary schools. The study was carried out in Al-Khobar city, Eastern region of Saudi Arabia. The findings revealed that the use of CAM by adolescents in their lifetime ranged from 1.6% for acupuncture to 58.6% for honey treatment, with significant differences between genders, except in the use of dietary supplements, black cumin, and acupuncture therapies. Females were more likely to use CAM for treating abdominal pains, cold and flu, and cough than males (P < 0.000). Family members and friends (67.7%) were the main source of CAM usage, followed by television (10%), and Internet (8%). Religious and medicinal herb healers were the CAM healers most commonly visited by adolescents. Nearly 21-43% of adolescents had positive attitudes toward CAM, with some significant differences between males and females. It can be concluded that CAM is widely used by Saudi adolescents, but caution should be exercised for the safe usage of some CAM treatments. CAM should not be ignored; however there is an urgent need to establish regulations for CAM usage.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.068
GPT teacher head0.441
Teacher spread0.373 · 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 source (direct Gemma or distilled Codex), 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

Citations38
Published2014
Admission routes1
Has abstractyes

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