Prevalence and Predictors of Self-Medication Practices in India: A Systematic Literature Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Self-Medication (SM) is a practice of using medications to treat selfdiagnosed symptoms without a legitimate prescription by a health care professional. Alongside posing a burden on a patient, SM practices are associated with certain unfavourable health conditions such as drug-resistance, adverse effects, drug-interactions, including death. OBJECTIVE: To systematically review and quantify the prevalence of SM practices and its associated factors in India. METHODS: A comprehensive systematic search was performed using scientific databases such as PubMed and Cochrane library for the peer-reviewed research articles that were conducted in India without any language and date restrictions. Studies which were cross-sectional by design and assessing the prevalence and predictors of SM practices in India were considered for the review, and all the relevant articles were screened for their eligibility. RESULTS: Of 248 articles, a total of 17 articles comprising of 10,248 participants were included in the meta-analysis. Overall, the mean prevalence of SM practices in India was observed to be 53.57%. Familiarity with the medication appears to be a major reason to practice SM (PR: 30.45; 95% Confidence Interval [CI]: 17.08-43.82; 6 studies), and the practice was noticed more among individuals from a middle-lower class family with a prevalence rate of 26.31 (95%CI: 2.02-50.60; P<0.0001). Minor ailments were the primary reason for practicing SM (PR: 42.46; 95%CI: 21.87- 63.06), among which headache was the most commonly reported (PR: 41.53; 95%CI: 18.05-65.02). CONCLUSION: Self-medication practices are quite frequent in India. While NSAIDs and anti-allergens are the most frequently utilized self-medicated drugs used for headache and cold and cough.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".