Low- and middle-income countries face many common barriers to implementation of maternal health evidence products
Bibliographic record
Abstract
OBJECTIVES: To explore similarities and differences in challenges to maternal health and evidence implementation in general across several low- and middle-income countries (LMICs) and to identify common and unique themes representing barriers to and facilitators of evidence implementation in LMIC health care settings. STUDY DESIGN: Secondary analysis of qualitative data. SETTING: Meeting reports and articles describing projects undertaken by the authors in five LMICs on three continents were analyzed. Projects focused on identifying barriers to and facilitators of implementation of evidence products: five World Health Organization maternal health guidelines, and a knowledge translation strategy to improve adherence to tuberculosis treatment. Data were analyzed using thematic content analysis. RESULTS: Among identified barriers to evidence implementation, a high degree of commonality was found across countries and clinical areas, with lack of financial, material, and human resources most prominent. In contrast, few facilitators were identified varied substantially across countries and evidence implementation products. CONCLUSION: By identifying common barriers and areas requiring additional attention to ensure capture of unique barriers and facilitators, these findings provide a starting point for development of a framework to guide the assessment of barriers to and facilitators of maternal health and potentially to evidence implementation more generally in LMICs.
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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 | 0.039 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".