Knowledge and practice regarding neonatal resuscitation and associated factors among nurses and midwives in Ethiopia: a systematic review protocol
Bibliographic record
Abstract
Abstract Background Neonatal resuscitation is critical newborn care that can save lives. Nurses and midwives play a critical role by assessing newborn condition and initiating resuscitation if necessary. Understanding the knowledge and practice of neonatal resuscitation, as well as the factors that influence them, is essential for designing effective strategies in resource-constrained settings. This systematic review will assess what nurses and midwives in Ethiopia know and do about neonatal resuscitation, and what factors may affect their knowledge and practice. Methods This systematic review will look for and gather data from a variety of databases, including JBI, Cochrane, MEDLINE, CINAHL, EMBASE, PEDro, POPLINE, Proquest, OpenGrey, Google Scholar, Google, APA PsycInfo, Web of Science, Scopus, and HINARI. It will also include unpublished studies and grey literature from a variety of sources. The review will only include quantitative observational or experimental studies that report on knowledge or practice of neonatal resuscitation. The Newcastle-Ottawa Quality Assessment Scale will be used to choose which studies are eligible. Two researchers will independently assess the quality of the included studies and extract the data. They will resolve any disagreements through discussion. Heterogeneity, or variation between the studies, will be assessed using forest plots and the I2 statistic. If there is a lot of heterogeneity, a random-effects model will be used to pool the data. Subgroup analysis will be used to explore the possible sources of this heterogeneity. Publication bias, or the tendency for studies with positive results to be more likely to be published, will be assessed using funnel plots and Egger's regression test. The statistical software package STATA version 17 will be used to conduct the meta-analysis. An association will be considered statistically significant if the p-value is less than 0.05. Discussion Neonatal mortality remains high in Ethiopia, especially in rural areas. Neonatal resuscitation is a key intervention that can save the lives of babies, but more research is needed to assess the knowledge and practice of neonatal resuscitation among nurses and midwives in Ethiopia. Systematic review registration number in PROSPERO: CRD42023459107.
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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 | 0.056 | 0.052 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.015 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.060 | 0.007 |
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".