Prevalence of Self-Medication Among the Elderly in Kermanshah-Iran
Bibliographic record
Abstract
INTRODUCTION: Self-medication is consumption of one or several medications without the physician's prescription. Given the risks of self-medication, this study was carried out to assess the prevalence of self-medication and its related factors among the elderly in Kermanshah-Iran METHOD: In this descriptive cross-sectional study, 272 elderly visiting the private offices in Kermanshah were selected through convenience sampling method. The instrument for data collection was a researcher made self-medication questionnaire. Data were analyzed using descriptive and analytic statistical methods (Chi-Square and Fisher exact test). RESULTS: The prevalence of self-medication was 83%. The most common reasons for self-medication were certainty of its safety (93%), prior consumption of the drug (87.6%), busy offices of physicians (82%), non-seriousness of the illness (77.8%) and prior experience of the disease (73%).The most common drugs used for self-medication were analgesics (92%), cold drugs (74%), vitamins (61%), digestive drugs (54%) and antibiotics (43%). There was a significant correlation between self-medication and gender (p=0.001), education level (p=008), drug information (p=0.01), marital status (p=0.002), and medical insurance (p=0.001) variables. CONCLUSION: considering the relatively high rates of self-medication among the elderly as well as its side effects, designing and performing educational programs are suggested for the elderly people.
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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 | 0.000 | 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".