Evaluating quality of obstetric care in low-resource settings: Building on the literature to design tailor-made evaluation instruments - an illustration in Burkina Faso
Bibliographic record
Abstract
BACKGROUND: There are many instruments available freely for evaluating obstetric care quality in low-resource settings. However, this profusion can be confusing; moreover, evaluation instruments need to be adapted to local issues. In this article, we present tools we developed to guide the choice of instruments and describe how we used them in Burkina Faso to facilitate the participative development of a locally adapted instrument. METHODS: Based on a literature review, we developed two tools: a conceptual framework and an analysis grid of existing evaluation instruments. Subsequently, we facilitated several sessions with evaluation stakeholders in Burkina Faso. They used the tools to develop a locally adapted evaluation instrument that was subsequently tested in six healthcare facilities. RESULTS: Three outputs emerged from this process:1) A comprehensive conceptual framework for the quality of obstetric care, each component of which is a potential criterion for evaluation.2) A grid analyzing 37 instruments for evaluating the quality of obstetric care in low-resource settings. We highlight their key characteristics and describe how the grid can be used to prepare a new evaluation.3) An evaluation instrument adapted to Burkina Faso. We describe the experience of the Burkinabé stakeholders in developing this instrument using the conceptual framework and the analysis grid, while taking into account local realities. CONCLUSIONS: This experience demonstrates how drawing upon existing instruments can inspire and rationalize the process of developing a new, tailor-made instrument. Two tools that came out of this experience can be useful to other teams: a conceptual framework for the quality of obstetric care and an analysis grid of existing evaluation instruments. These provide an easily accessible synthesis of the literature and are useful in integrating it with the context-specific knowledge of local actors, resulting in evaluation instruments that have both scientific and local legitimacy.
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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 | 0.189 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".