Pain management practice and associated factors among nurses working in Ethiopia: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Pain management is a crucial component of patient care that promotes relaxation, lowers complications, improves quality of life, and shortens hospital stays. Several studies assessed the nurses' pain management practices in Ethiopia. However, the findings of these studies are highly variable and inconsistent. Therefore, the study aims to determine the overall prevalence of pain management practice and associated factors among nurses working in Ethiopia. METHOD: The study included all observational quantitative research articles conducted among nurses in Ethiopia. We used Google Scholar, PubMed, Cochrane Library, and Scopus searching databases. The Newcastle-Ottawa Scale was used to check the study quality. Then I2 statistics and Cochran's Q test were used to evaluate heterogeneity. The Funnel, Egger's test, and non-parametric trim and fill effect tests were used to check publication bias by using a random effect model. Finally, we conducted subgroup analysis and sensitivity tests to evaluate statistical heterogeneity and the presence or absence of any influential study. RESULT: In the final analysis, we included eighteen studies, and 4,213 nurses participated. The overall pooled prevalence of nurses with good pain management practice was 43.79% (95% CI: 38.52, 49.06%). In-service training AOR; 95% CI 3.21 (1.87, 4.54), good knowledge AOR; 95% CI 2.44 (1.78, 3.09), positive attitudes AOR; 95% CI 2.84 (1.24, 4.44), and pain management guidelines in health facilities AOR; 95% CI 3.46 (1.48, 5.44) were the significant associated factors with pain management practice among nurses in Ethiopia. CONCLUSION: This study found that over half of Ethiopian nurses had poor pain management practices. Knowledge, attitude, training, and pain management guidelines in health facilities were significant factors. Therefore, health managers and stakeholders should prioritize pain management awareness, attitudes, availability of guidelines, and in-service training to improve patient care and outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".