A report of Kabul internet users on self-medication with over-the-counter medicines
Bibliographic record
Abstract
Self-medication (SM) with over-the-counter (OTC) medications is a prevalent issue in Afghanistan, largely due to poverty, illiteracy, and limited access to healthcare facilities. To better understand the problem, a cross-sectional online survey was conducted using a convenience sampling method based on the availability and accessibility of participants from various parts of the city. Descriptive analysis was used to determine frequency and percentage, and the chi-square test was used to identify any associations. The study found that of the 391 respondents, 75.2% were male, and 69.6% worked in non-health fields. Participants cited cost, convenience, and perceived effectiveness as the main reasons for choosing OTC medications. The study also found that 65.2% of participants had good knowledge of OTC medications, with 96.2% correctly recognizing that OTC medications require a prescription, and 93.6% understanding that long-term use of OTC drugs can have side effects. Educational level and occupation were significantly associated with good knowledge, while only educational level was associated with a good attitude towards OTC medications (p < 0.001). Despite having good knowledge of OTC drugs, participants reported a poor attitude towards their use. Overall, the study highlights the need for greater education and awareness about the appropriate use of OTC medications in Kabul, Afghanistan.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".