Prevalence and Correlates of Self-Medication Practices for Prevention and Treatment of COVID-19: A Systematic Review
Bibliographic record
Abstract
It has been suggested that the COVID-19 pandemic led to an increase in self-medication practices across the world. Yet, there is no up-to-date synthesized evidence on the prevalence of self-medication that is attributable to the pandemic. This study aimed to conduct a systematic literature review on the prevalence and correlates of self-medication for the prevention and treatment of COVID-19 globally. The review was registered with the PROSPERO database. Searches were conducted following PRISMA guidelines, and relevant articles published between 1 April 2020 and 31 March 2022 were included. Pooled prevalence rate was conducted using the Meta package in R. A total of 14 studies from 14 countries, which represented 15,154 participants, were included. The prevalence of COVID-19-related self-medication ranged from 3.4-96%. The pooled prevalence of self-medication for this purpose was 44.9% (95% CI: 23.8%, 68.1%). Medications reported by studies for self-medication were antibiotics (79%), vitamins (64%), antimalarials (50%), herbal and natural products (50%), analgesics and antipyretics (43%), minerals and supplements (43%), cold and allergy preparations (29%), corticosteroids (14%), and antivirals (7%). The prevalence of self-medication with antibiotics is concerning. More public health education about responsible self-medication amidst the COVID-19 pandemic and future pandemics is required to mitigate the rising threat of antimicrobial resistance.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".