Implementation of the WHO Safe Childbirth Checklist: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: The WHO Safe Childbirth Checklist (WHO SCC) was developed to accelerate adoption of essential practices that prevent maternal and neonatal morbidity and mortality during childbirth. This study aims to summarise the current landscape of organisations and facilities that have implemented the WHO SCC and compare the published strategies used to implement the WHO SCC implementation in both successful and unsuccessful efforts. METHODS AND ANALYSIS: This scoping review protocol follows the guidelines of the Joanna Briggs Institute. Data will be collected and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews report. The search strategy will include publications from the databases Scopus, PubMed, Embase, CINAHL and Web of Science, in addition to a search in grey literature in The National Library of Australia's Trobe, DART-Europe E-Theses Portal, Electronic Theses Online Service, Theses Canada, Google Scholar and Theses and dissertations from Latin America. Data extraction will include data on general information, study characteristics, organisations involved, sociodemographic context, implementation strategies, indicators of implementation process, frameworks used to design or evaluate the strategy, implementation outcomes and final considerations. Critical analysis of implementation strategies and outcomes will be performed with researchers with experience implementing the WHO SCC. ETHICS AND DISSEMINATION: The study does not require an ethical review due to its design as a scoping review of the literature. The results will be submitted for publication to a scientific journal and all relevant data from this study will be made available in Dataverse. TRIAL REGISTRATION NUMBER: https://doi.org/10.17605/OSF.IO/RWY27.
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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 | 0.183 | 0.138 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.074 | 0.021 |
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".