Utilization of nonpharmacological labor pain management and associated factors among healthcare providers in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
Health outcomes are a global priority, and the use of nonpharmacological methods for labor pain relief is recommended to improve these outcomes. However, there is a lack of a review regarding on the utilization of nonpharmacological labor pain management. this study aimed to assess the pooled utilization of nonpharmacological labor pain management and the associated factors among healthcare providers in Ethiopia. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed for this review. A total of 2837 articles were retrieved from international databases, including Scopus, PubMed, Web of Science, Science Direct, and National Digital Library repositories. The search for articles was conducted from February 1, 2024, to February 30, 2024. To assess the methodological quality Newcastle Ottawa Scale was utilized. Data extraction was performed using Excel, and the analysis was conducted with Stata 11 software.The effect size measurement utilization of nonpharmacological labor pain management was estimated using the Random Effect Mode. The Cochran’s Q test and I 2 statistic were used to assess the heterogeneity of studies. The symmetry of the funnel plot and Egger’s test were used to check for publication bias. Fourteen studies met the eligibility criteria with a total sample size of 4,821. The overall pooled utilization of nonpharmacological labor pain management among healthcare provider was 45.48% (95% CI: 35.74–55.22). Healthcare providers aged 20–29 years (AOR: 4.10; 95% CI: 1.79–9.39), those having with knowledge about nonpharmacological pain management (AOR: 3.11; 95% CI: 1.88–5.16), and those who allowed companions to support laboring mothers (AOR: 3.37; 95% CI: 1.56–7.24) were determinants of outcome variables. Over half of Ethiopian healthcare providers did not use nonpharmacological pain management during childbirth. Key factors healthcare providers aged 20–29, those with adequate knowledge, and healthcare providers’ ability who allowed companions to enter labor ward to support laboring mothers. Providing updated in-service training programs for older healthcare providers are recommended to utilize nonpharmacological pain management techniques. Policymakers should also create clear understand about obstetric guidelines to promote pain management during childbirth.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.031 |
| Bibliometrics | 0.009 | 0.009 |
| 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) | 0.002 | 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".