Experiences of Birth Attendants on Upward Obstetric Emergency Referrals in Low- and Middle-Income Countries: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Every day, approximately 800 women die from pregnancy-related causes, alongside 2.6 million stillbirths and 2.8 million neonatal deaths annually. Inadequate referral by skilled birth attendants hinders timely access to necessary emergency obstetric care, challenging progress toward the maternal health Sustainable Development Goal (SDG) 3. The COVID-19 pandemic further disrupted care in low- and middle-income countries, forcing women to rely on traditional birth attendants, thereby affecting the referral system. It is crucial to understand the experiences of both skilled and traditional birth attendants regarding upward referrals in emergency obstetric care to identify barriers and facilitators within these systems in low- and middle-income countries. OBJECTIVE: This study aims to map existing evidence on the experiences of skilled and traditional birth attendants regarding upward referral systems in emergency obstetric care within low- and middle-income countries. METHODS: We will conduct a scoping review guided by the Joanna Briggs Institute's methodological framework. Studies will be included if they report on experiences with upward referral in obstetrical emergencies. We will consider studies published in English and French from 2016 to July 2024. The literature search will be conducted in databases including PubMed, EBSCOhost (Academic Search Complete and CINAHL with full text), Scopus, Web of Science, and Google Scholar. Identified citations will be managed using EndNote version 21 (Clarivate Analytics) and Rayyan. Two independent reviewers will screen eligible studies and resolve disagreements through discussion with a third reviewer. Data will be extracted using a validated form and analyzed through content analysis, with findings presented narratively. This protocol aligns with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. The review will offer a comprehensive narrative of upward referral systems in obstetrical emergencies, focusing on transitions from traditional birth attendants to health care facilities and from lower to higher levels of health care. RESULTS: The preliminary search was completed in August 2024, and the database search will be conducted within the next 6 months. Findings will be disseminated through medical education conferences and publications. CONCLUSIONS: This review contributes a comprehensive narrative of upward referral systems in obstetrical emergencies, aiming to enhance understanding and improve transitions from traditional birth attendants to health care facilities and between different health care levels. It could significantly impact maternal and neonatal care by identifying the referral experiences of both skilled and traditional birth attendants. The insights may inform interventions that integrate traditional birth attendants into health care systems, potentially reducing maternal and neonatal mortality. The results will guide future research aimed at developing a model to improve upward referrals for obstetric emergencies in sub-Saharan Africa. TRIAL REGISTRATION: Open Science Framework 4HAVZ; https://osf.io/4havz. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/64886.
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 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 | 0.083 | 0.068 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.059 | 0.009 |
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".