Medication Errors in Ethiopia: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background: The caution of medication prescription and administration are the main physician and nursing services though there was no study to show medication error at the nation level in Ethiopia. Therefore, we estimated the national prevalence of medication errors. Methods: A systematic review of studies searched in PubMed, Scopus, African Journal of Online, and Google Scholar was done. Newcastle-Ottawa quality assessment scale was used to assess the quality status of the included studies. We employed Galbraith plot and Egger’s regression test to assess publication bias. The national prevalence of medication errors was estimated using a random-effects model meta-analysis. Moreover, subgroup analysis and meta-regression analyses were done to explore the reason of statistical heterogeneity.Results: A total of 14 studies with 5,552 administered medications and 5,661 prescription sheets were included. The overall prevalence of medication error in Ethiopia was 57.6% (95% CI: 46.2, 69.0). The pooled burden of medication administration and prescription error was 58.4% (95% CI: 51.4, 65.5) and 55.8% (95% CI: 27.0, 84.6), respectively. Omission error (38%), wrong dose (38.5%), and the wrong combination of drugs (28.7%) were highly reported types of prescription errors, whereas missed doses (57.0%), technical errors (47.0%), wrong time (35.0%), and wrong dose (30.0%) were frequently observed medication administration errors.Conclusions: Medication errors were very common in Ethiopian hospitals whereby at least one out of two medications were wrongly prescribed and administered. Our review provided a shred of up-to-date evidence for clinicians, regional, and national healthcare policymakers to appraise and improve the quality of hospitals’ inpatient care.Trail registration: The protocol is registered in the Prospero database with a registration number of CRD42019138125.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".