Outcome Assessment of a Dedicated HIV Positive Health Care Worker Clinic at a Central Hospital in Malawi: A Retrospective Observational Study
Bibliographic record
Abstract
BACKGROUND: Malawi has one of the world's lowest densities of Health Care Workers (HCW) per capita. This study evaluates outcomes of a dedicated HCW HIV clinic in Malawi, created at Zomba Central Hospital in January 2007. METHODS AND FINDINGS: Retrospective cohort data was analyzed comparing HCW clinic patient baseline characteristics and treatment outcomes at 18 months after inception, against those attending the general HIV clinic. In-depth interviews and focus group discussions were conducted to explore perceptions of patients and caregivers regarding program value, level of awareness and barriers for uptake amongst HCW. 306 patients were enrolled on antiretroviral therapy (ART) in the HCW HIV clinic, 6784 in the general clinic. Significantly (p<0.01) more HCW clients were initiated on ART on the basis of CD4 as opposed to WHO Stage 3/4 (36% vs.23%). Significantly fewer HCW clients defaulted (6% vs.17%), and died (4% vs.12%). The dedicated HCW HIV clinic was perceived as important and convenient in terms of reduced waiting times, and prompt and high quality care. Improved confidentiality was an appreciated quality of the HCW clinic however barriers included fear of being recognized. CONCLUSIONS/SIGNIFICANCE: Outcomes at the HCW clinic appear better compared to the general HIV clinic. The strategy of dedicated clinics to care for health providers is a means of HIV impact mitigation within human resource constrained health systems in high prevalence settings.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".