Criterion-based clinical audit to assess quality of obstetrical care in low- and middle-income countries: a systematic review
Bibliographic record
Abstract
PURPOSE: Low-quality obstetric care in low- and middle-income countries contributes to high in-hospital maternal mortality. Criterion-based clinical audits are increasingly used to measure and improve obstetric care in these settings. This article systematically reviews peer-reviewed literature to determine if these audits are feasible, valid and reliable measurement tools for assessing the quality of obstetric care. DATA SOURCES: PUBMED, Google Scholar and Web of Science databases were searched for peer-reviewed articles published between 1995 and 2009 and which used criterion-based clinical audits to measure the quality of obstetric care in low- and middle-income countries. STUDY SELECTION: Sixty-nine studies were identified by key terms and subsequently reviewed. Ten were retained based on inclusion/exclusion criteria. DATA EXTRACTION: (i) General characteristics of the study; (ii) compliance with expected standards of care and on maternal/child health outcomes; (iii) selection of the study population and sampling methods; and (iv) quality control and reliability. RESULTS OF DATA SYNTHESIS: Criterion-based clinical audit is increasingly used in low- and middle-income countries. Most audits were conducted in sub-Saharan Africa. Studies had cross-sectional study or before-and-after designs. Sampling methods were poorly reported and selection bias was a concern. No studies compared audit against other measures of quality of care or against patient outcomes. METHODS: for quality control and assurance were generally not documented and reliability was mostly unaddressed. CONCLUSIONS: Criterion-based clinical audit appears feasible. No studies have rigorously evaluated its measurement properties in low- and middle-income countries. Without such evaluation, measurement properties of the audit remain under question.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".