Rational Drug Use Evaluation Based on World Health Organization Core Drug Use Indicators in Ethiopia: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Rational use of medicines plays a vital role in avoiding preventable adverse drug effects, maximizing therapeutic outcomes with promoting patient adherence, and minimizing the cost of drug therapy. Irrational use of drugs is often observed in countries with weak health care systems. No review has been done that systematically expresses rational drug use practice based on the three WHO core drug use indicators in Ethiopia. Thus, this study was aimed to review systematically the prescribing, health-facility, and patient-care indicators based on WHO core drug use indicators in Ethiopia. METHODS: A systematic article search was conducted in different electronic databases including PubMed/ MEDLINE, the Cochrane Library, EMBASE, Web of Science, POPLINE, the Global Health, and Google scholar. Quality assessment was conducted using Newcastle-Ottawa quality assessment scale. Studies were synthesized and grouped in to prescribing, patient care and health facility indicators. RESULTS: From a total of 6239 articles, 21 studies were found suitable for the review. The highest average number of drugs per encounter was 2.5 while the lowest was 0.98. The percentage of generic drug use was ranged from 70.5% to 100%. The highest percentage of encounters with an antibiotic was 85%. The lowest percentage of drugs prescribed from essential drugs list was 81.4%. The highest percentage of drugs actually dispensed and adequately labeled was 96.16% and 96.25%, respectively. CONCLUSION: This study showed that the practice of rational drug use varied across region of the country. The average number of drugs per prescription, percentage of drugs encounter with antibiotics, drugs prescribed by their generic name, average consultation time, average dispensing time, percentage of drugs adequately labeled, and availability of essential drugs showed deviation from the standard recommended by WHO. Thus, provision of regular training for prescribers and pharmacists, and ensuring the availability of essential drugs should be encouraged.
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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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".