Patient-level benefits associated with decentralization of antiretroviral therapy services to primary health facilities in Malawi and Uganda
Bibliographic record
Abstract
Background: The Lablite project captured information on access to antiretroviral therapy (ART) at larger health facilities ('hubs') and lower-level health facilities ('spokes') in Phalombe district, Malawi and in Kalungu district, Uganda. Methods: We conducted a cross-sectional survey among patients who had transferred to a spoke after treatment initiation (Malawi, n=54; Uganda, n=33), patients who initiated treatment at a spoke (Malawi, n=50; Uganda, n=44) and patients receiving treatment at a hub (Malawi, n=44; Uganda, n=46). Results: In Malawi, 47% of patients mapped to the two lowest wealth quintiles (Q1-Q2); patients at spokes were poorer than at a hub (57% vs 23% in Q1-Q2; p<0.001). In Uganda, 7% of patients mapped to Q1-Q2; patients at the rural spoke were poorer than at the two peri-urban facilities (15% vs 4% in Q1-Q2; p<0.001). The median travel time one way to a current ART facility was 60 min (IQR 30-120) in Malawi and 30 min (IQR 20-60) in Uganda. Patients who had transferred to the spokes reported a median reduction in travel time of 90 min in Malawi and 30 min in Uganda, with reductions in distance and food costs. Conclusions: Decentralizing ART improves access to treatment. Community-level access to treatment should be considered to further minimize costs and time.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".