Prevalence and Causes of Self Medication among Medical Students of Kerman University of Medical Sciences, Kerman, Iran
Bibliographic record
Abstract
<p><strong>BACKGROUND:</strong> Self-medication is a public health concern because of drug misuse/abuse and its medical, social and psychological problems.</p><p><strong>AIM: </strong>Given the growing prevalence of self-medication, the present study aims to determine the prevalence and causes of self-medication among students of Kerman University of Medical Sciences.</p><p><strong>METHOD:</strong> This cross-sectional study was conducted in 2014 on 550 students who were selected through multistage sampling from Kerman University of Medical Sciences, Kerman, in southeast Iran. Data was collected through a self-report questionnaire. Test-retest reliability and content validity of this questionnaire were confirmed. Data analysis was carried out using descriptive and inferential statistics via t-test and analysis of variance (ANOVA).<strong></strong></p><p><strong>RESULTS:</strong> The prevalence of self-medication among students was 50.2%. The most common cause of self-medication was related to students' knowledge about the diseases and medications (58.7%). The majority of drugs consumed arbitrarily included cough and cold medications (94.5%); analgesics (89.9%); antihistamines (80.0%); herbal drugs and distillates (78.9%); vitamins, minerals, dietary supplements and energizers (71.5%); antibiotics (61.8%); and gastrointestinal drugs (54.9%), respectively. The most common illness that led to self-medication was the common cold (95.4%), and the most important source of information regarding self-medication was the students’ own scientific knowledge of medical drugs (80.6%).</p><p><strong>CONCLUSION:</strong> Due to the adverse effects of self-medication, drug dependency, and microbial resistance and the relatively high prevalence of self-medication among students in this study, it would be advisable to organize awareness campaigns to further educate students about self-medication.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".