Comparing program supervision with an external RADAR evaluation of quality of care in integrated community case management for childhood illnesses in Mali
Bibliographic record
Abstract
BACKGROUND: Many countries have adopted integrated community case management (iCCM) to reduce mortality among children under five years from common childhood illnesses. The 2016-2020 Malian Red Cross iCCM program trained 441 Community Health Workers (CHWs) to treat malaria, pneumonia, diarrhea, and malnutrition for children under five years of age in six districts. Implementation strength and quality of care (QoC) were assessed through the program's supervision function, using the Malian Ministry of Health's system. OBJECTIVE: This paper compares methods and results of program supervision data and an independent evaluation to assess the effectiveness of program implementation and supervision and inform program improvement. It also presents the benefits and limitations of each method. METHOD: . RADAR evaluation data collected in July and August 2018 were compared with program supervision data collected mostly between May and December 2018. RESULTS: The RADAR evaluation provided detailed findings on correct assessment, classification, and treatment per illness, medication type, and dosage. Program supervision combined the findings for all illnesses, medication type, and dosage due to limitations in the data collection process. Six indicators were comparable between both methods. Findings were similar for temperature and mid-upper arm circumference measurements but diverged between program supervision and the RADAR evaluation, respectively, on correct classification for all illnesses (87.1% vs. 65.3%), correct treatment for all illnesses (69.5% vs. 39.8%), correct respiratory rate counting (88.5% vs. 54.7%), and administering the first dose by CHW (75.4% vs. 65.0%). Findings from the RADAR evaluation guided improvements in program supervision. CONCLUSIONS: A robust program supervision system can serve as a credible method to assess QoC. However, a rigorous independent QoC evaluation provides a valuable benchmark to gauge the effectiveness of the supervisory process.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".