Attitude and Practices of self-medication among the students of Sialkot Medical College, Sialkot
Bibliographic record
Abstract
Background: self-medication is becoming increasingly prevalent in our daily lives, yet it is a dangerous and harmful activity. Aim: To determine the prevalence and knowledge of self-medication among the medical students of Sialkot Medical College. Methods: A descriptive study in which total sample of 500 medical students were approached. Only, 349 students had filled the questionnaire. Regarding sampling technique, all the students currently enrolled in MBBS from session 2018 to 2022 of SMC were included. Data was collected using self-structured questionnaire. Data was analyzed using SPSS version 25. Results: Self-medication was found to be 83.8% during the last 1 year. Most commonly used medications include analgesic 73.2%, antibiotics 36.6%, gastric acidity medication 25.5%, anti-emetics and antitussives. Commonly experienced symptoms include; headache (73.6%), fever and flu (58%), cough (30.9%), gastric acidity (24.8%) and others. Source of information include family and friends (56.1%), medical text books (18.2%), internet (11.5%) and others. About 46.2% students felt that problem was not serious to consult a physician and 29% reported personal convenience. Conclusion: Prevalence of self-medication was high among the undergraduate university students of Sialkot Medical College, Sialkot. Keywords: Self-medication, prevalence, attitude
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".