The Prevalence of Self-Medication With Painkillers Among Iraqi Medical Students
Bibliographic record
Abstract
BACKGROUND: The use of painkillers is widespread worldwide, some people experience unwanted side effects, and some may overuse them. Self-medication is the selection and use of medicines by individuals to treat self-recognized illnesses and collateral symptoms. OBJECTIVES: We aim to determine the prevalence and pattern of self-medication with painkillers among a population of undergraduate medical students in Baghdad. METHODS: We carried out a cross-sectional study, via convenient sampling, among 502 medical students (n=502) from the University of Baghdad, Al-Mustansirya University, Al-Kindy University, Al-Nahrain University, and Al-Iraqia University. We distributed an anonymous online questionnaire to the students. The survey included questions on demographic variables and information on self-medicating with painkillers during the academic year of 2018-2019. RESULTS: The prevalence of use of painkillers was 68.73%, 73% were females, and 27% were males. There was a statistically significant association between gender and the use of analgesics. Still, there was no statistically significant association between the academic level of students and analgesic use. The frequency of analgesic use per month was less than once (34.5%) of the participants, 1-3 times (37.1%), 4-6 times (14.2%), 7-9 times (7%), 10-12 times (3.1%), and more than 13 times (4%) of the participants. Most of the respondents (68%) reported that there was no difference in use between regular college days and exam days. The most common cause of use was headache (71%) for males and females, while dysmenorrhea was the second most common cause among female participants (36%). The most common source of information about analgesics relied on by the respondents was from friends (50.1%), family members, pharmacists, textbooks, the internet, and nurses. The most commonly used drug was Acetaminophen. CONCLUSION: Self-medication with analgesics is highly prevalent among undergraduate medical students in Baghdad, and we need to raise the awareness of the public on the potentials of addictive behavior.
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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) | 0.003 | 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".