Facilitators and barriers to the effective implementation of the individual maternal near-miss case reviews in low/middle-income countries: a systematic review of qualitative studies
Bibliographic record
Abstract
BACKGROUND: The maternal near-miss cases review (NMCR), a type of clinical audit, proved to be effective in improving quality of care and decreasing maternal mortality in low/middle-income countries (LMICs). However, challenges in its implementation have been described. OBJECTIVES: Synthesising the evidence on facilitators and barriers to the effective implementation of NMCR in LMICs. DESIGN: Systematic review of qualitative studies. DATA SOURCES: MEDLINE, LILACS, Global Health Library, SCI-EXPANDED, SSCI, Cochrane library and Embase were searched in December 2017. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Qualitative studies exploring facilitators and/or barriers of implementing NMCR in LMIC were included. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers extracted data, performed thematic analysis and assessed risk of bias. RESULTS: Out of 25 361 papers retrieved, 9 studies from Benin, Brazil, Burkina Faso, Cote D'Ivoire, Ghana, Malawi, Morocco, Tanzania, Uganda could be included in the review. The most frequently reported barriers to NMCR implementation were the following: absence of national guidelines and local protocols; insufficient training on how to perform the audit; lack of leadership, coordination, monitoring and supervision; lack of resources and work overload; fear of blame and punishment; poor knowledge of evidenced-based medicine; hierarchical differences among staff and poor understating of the benefits of the NMCR. Major facilitators to NMCR implementation included: good leadership and coordination; training of all key staff; a good cultural environment; clear staff's perception on the benefits of conducting audit; patient empowerment and the availability of external support. CONCLUSIONS: In planning the NMCR implementation in LMICs, policy-makers should consider actions to prevent and mitigate common challenges to successful NMCR implementation. Future studies should aim at documenting facilitators and barriers to NMCR outside the African Region.
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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 | 0.110 | 0.242 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".