Prevalence and predictors of over-the-counter medication use among adolescents in the United Arab Emirates
Bibliographic record
Abstract
RSUM Les formes de l'automdication chez les adolescents des mirats arabes unis (EAU) demeurent largement sousdocumentes. La prsente tude a pour objectifs de : 1) dterminer le profil de l'automdication chez les adolescents des mirats arabes unis ; et 2) de dterminer les facteurs prdictifs biologiques ou physiques, psychologiques ou comportementaux, et sociaux de cette pratique parmi la population d'adolescents des mirats arabes unis. l'aide d'un modle d'tude transversale, des donnes ont t collectes sur la prvalence de l'automdication auprs d'un chantillon de 6363 adolescents. Au total, 51 % des participants de cette tude ont rapport avoir recours une mdication sans ordonnance. La forme la plus courante concernait le paractamol. Les facteurs prdictifs significatifs de mdication sans ordonnance taient les suivants : la nationalit (mirats arabes unis, pays du Conseil de Coopration du Golfe/Moyen-Orient, arabes/africains, occidentaux et autres) ; un besoin en soins de sant non satisfait ; le sexe (femme) ; l'ge (15-18ans) ; tous types de diagnostic mdical ; un usage non conventionnel des mdicaments ; le fait de passer plus de cinq heures devant la tlvision ou l'ordinateur chaque jour ; et de consommer des mdicaments dlivrs sur ordonnance. Il est donc ncessaire de mettre au point des politiques et des stratgies de sant publique qui fassent la promotion d'un usage appropri des mdicaments dlivrs sans ordonnance dans la population.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".