IMPROVING THE ABILITY OF DISTRICTS IN UGANDA TO MONITOR THEIR HIV PROGRAMS
Bibliographic record
Abstract
BACKGROUND: Although district health teams (DHT) in Uganda are supposed to monitor and support facilities to ensure quality HIV data collection, reporting and use, they are often ill-equipped to do so. We implemented a program designed to build the capacity of districts to manage and use their own HIV-related program data and to assist facilities to collect and evaluate their own data. METHODS: We conducted a baseline assessment of the monitoring and evaluation (M&E) capacity of 38 districts. In the 10 worst-performing districts, we identified and trained district-level staff to become M&E mentors who in turn trained and supervised facility-level staff. We collected information on action plans developed by facilities to address major issues of concern. Following the intervention, we reassessed M&E capacity of the 10 targeted districts. RESULTS: Among the 38 districts assessed, one-half did not have a biostatistician, less than one-quarter had staff trained in the basics of M&E or data analysis, and less than one-quarter had an M&E plan. The main concerns of facilities included lack of updated data collection tools, lack of supervision, inaccurate data recording, and limited ability to analyze and use data. In the 10 targeted districts, comparison before and after the intervention showed that the number of districts with trained M&E staff increased (4 to 9), the number of M&E plans increased (3 to 6), and the number using data for programming increased (4 to 8). Implementation of action plans by facilities successfully addressed many issues and led to improved programming. CONCLUSION: Challenges of district M&E in Uganda mainly result from a lack of skilled human resources. On-the-job training and direct involvement of district staff to provide support to facilities can lead to improvements in data quality and use.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".