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Record W3090476879 · doi:10.2196/18935

Mapping Evidence of Neonatal Resuscitation Training on the Practices of Unskilled Birth Attendants in Low-Resource Countries: Protocol for a Scoping Review

2020· review· en· W3090476879 on OpenAlexvenueno aff
Adenike Adebola Olaniyi, Busisiwe P. Ncama, Hafaza Amod

Bibliographic record

VenueJMIR Research Protocols · 2020
Typereview
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersInyuvesi Yakwazulu-Natali
KeywordsProtocol (science)Neonatal resuscitationMedicineDeveloping countryResource (disambiguation)Training (meteorology)NursingResuscitationMedical emergencyComputer scienceAlternative medicineEconomic growthGeographyEmergency medicineEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Competence in neonatal resuscitation of the newborn is very critical to ensure the safety and well-being of newborn infants. The acquisition of neonatal resuscitation skills by birth attendants improves self-efficacy, thereby reducing neonatal mortality as a result of asphyxia. Approximately one-quarter of all neonatal deaths globally are caused by birth asphyxia. The need for neonatal resuscitation is most imperative in resource-constrained settings, where access to intrapartum obstetric care is inadequate. OBJECTIVE: This protocol describes the methodology of a scoping review on evidence of training in neonatal resuscitation and its association with practice in low-resource countries. The aim of the review is to map the available evidence of neonatal resuscitation training on the practices of unskilled birth attendants. METHODS: The scoping review will use the Population, Concept, and Context (PCC) framework proposed by Arksey and O'Malley, refined by Levac et al, and published by Joanna Briggs Institute, while following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. The search strategy was developed with the assistance of the college librarian. A number of databases of peer-reviewed research (PsycINFO and Wiley Online Library [via EBSCOhost], PubMed, MEDLINE with full text, Google Scholar [via ScienceDirect], and CINAHL Plus with full text [via EBSCOhost]) and databases committed to grey literature sources will be searched, and reference extraction will be performed. Two independent reviewers will screen and extract data, and discrepancies will be resolved by a third reviewer. The extracted data will undergo a descriptive analysis of contextual data and a quantitative analysis using appropriate statistical methods. RESULTS: Data relating to neonatal resuscitation training and practices in low-resource settings will be extracted and included for analysis. We expect that the review will be completed 12 months from the publication of this protocol. CONCLUSIONS: This scoping review will focus on the review of evidence and provide an insight into the existing literature to guide further research and identify implementation strategies to improve the practices of unskilled birth attendants through the acquisition of skills and self-efficacy in neonatal resuscitation. The results of this review will be presented at relevant conferences related to newborn and child health and neonatal nursing studies and published in a peer-reviewed journal. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/18935.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.085
metaresearch head score (Gemma)0.110
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.085
Threshold uncertainty score0.450

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0850.110
Meta-epidemiology (narrow)0.0060.005
Meta-epidemiology (broad)0.0160.021
Bibliometrics0.0200.020
Science and technology studies0.0050.006
Scholarly communication0.0100.009
Open science0.0060.008
Research integrity0.0100.007
Insufficient payload (model declined to judge)0.0520.008

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.

Opus teacher head0.584
GPT teacher head0.645
Teacher spread0.061 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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