Self-medication: Prevalence among Undergraduates in Kathmandu Valley
Bibliographic record
Abstract
Background: Self-medication is defined as the use of medicines to treat self-recognized or self-diagnosed conditions or symptoms, instead of seeking advice from professionals. Aim: Our study was aimed to assess knowledge, attitude and practice of self-medication among undergraduates in different colleges of Kathmandu valley. Methods: Descriptive cross-sectional study was carried out among undergraduates of Kathmandu valley. A semi-structured questionnaire was distributed to students in 4 different colleges. Chi-square test was used to determine statistical significance. Likert’s scale was used to measure attitude. Results: 240 students participated in this study. Totally, (92.9%) students had known and taken medicine without doctor’s prescription. More than half of the participants (56.6%) had good knowledge on self-medication and nearly three quarter (74.7%) of the respondents had a positive attitude regarding self-medication. Fever, cough/cold and aches/pain were the most common symptoms for self-medication, thus making antipyretics and analgesics the most popular self-medication drugs. Pharmacists and family were the major source of information regarding self-medication. Conclusion: Based on the findings, the prevalence of self-medication was high (94.9%). Majority respondents had good knowledge of the advantages and disadvantages of self-medication but still practiced it.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".