Promising practices for adapting and implementing the WHO Safe Childbirth Checklist: case studies from India and Rwanda
Bibliographic record
Abstract
Background The World Health Organization (WHO) Safe Childbirth Checklist (SCC) was published in 2015 as a patient safety tool to improve facility-based childbirth care through boosting adherence with essential practices around the major causes of maternal and newborn morbidity and mortality. We brought together partners who led implementation of the SCC in India and Rwanda to: (i) contextualize the findings from surveys and interviews about SCC adaptation and implementation around the world (data published separately) with our partners’ implementation experiences in India and Rwanda, and (ii) identify promising practices for SCC implementation. Methods We identified two partners–one from Tamil Nadu, India and one from Masaka District, Rwanda–to work together in identifying key promising practices regarding the SCC based on their direct experiences and data we collected from other implementers around the world. From June-September 2020, we held 4 virtual design workshops using brainwriting exercises to explore promising practices for adaptation and implementation of the SCC. We consolidated the implementation experiences in India and Rwanda into the WHO SCC Implementation Guide phases of Engage, Launch, and Support, and included two additional phases: Project Design and Evaluation. Results We present two case studies of SCC implementation that demonstrate improved adherence with essential birth practices after implementation of the SCC. Based on the case studies, we developed promising practices according to five implementation stages: Project Design, Engage, Launch, Support, and Evaluation. Clarifying the purpose and users of the tool, applying human-centered design principles, and developing evaluation plans for the specified purpose were some promising practices that emerged. Conclusions Our partnership with direct implementers of the SCC yielded important insights about how to adapt, implement, evaluate, and sustain use of the Checklist. Such partnerships are critical in building an evidence base for promising practices regarding SCC implementation around the world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".