Updating a patient-level ART database covering remote health facilities in Zomba district, Malawi: lessons learned
Bibliographic record
Abstract
SETTING: A non-governmental organization, Dignitas International, working in partnership with the Ministry of Health in Malawi, adopted innovative, low-technology methods to collect, capture, and manage patient-level antiretroviral therapy (ART) data in a district database covering 26 remote low-resource facilities in Zomba District, Malawi. OBJECTIVE: To establish a longitudinal, observational database of routinely collected program data that could serve as a program monitoring and evaluation tool as well as a platform to conduct effective operational research. DESIGN: This article describes the processes developed for digital capture of paper-based ART clinical records at health facilities and updating them in a central electronic database. It documents and focuses on lessons learned during the implementation and review of processes. CONCLUSIONS: Data quality can only be ensured with regular review of, and compliance with, clearly delineated workflow protocols and adequate staffing and supervision. Through the implementation of this procedure, we expect to improve data quality, completeness, and use of routine ART clinical data in low-resource settings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".