Inter-rater agreement of scores to assess quality of care in public sector primary health care facilities – A pattern of performance
Bibliographic record
Abstract
PURPOSE: To determine if the scores obtained from the Ideal Clinic Assessment Tool (ICAT) used to assess the quality of care in public Primary Health Care facilities in South Africa showed inter-rater agreement between self-assessments, district peer reviews and cross-district peer reviews. The ICAT scores obtained in the three types of reviews were paired as follows: self-assessments/district peer reviews, self-assessment/cross-district peer reviews and district/cross-district peer reviews. The global scores and averages of the Vital elements for the three paired reviews for 587 facilities across the country were compared using Bland-Altman plots. RESULTS: The Bland-Altman plots showed no inter-rater agreement between the global scores and averages of the Vital elements for the facilities in any of the paired reviews (n = 1 761 reviews). Similarly, there was no inter-rater agreement between the global scores of the three paired reviews in any of the nine provinces in the country. CONCLUSION: There is still a need to continue to conduct both district and cross-district reviews despite the substantial cost of doing so. Further studies are required to determine what factors contributed to the disagreement in scores between the different types of reviews despite the preparatory training of reviewers.
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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 | 0.204 | 0.330 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".