Impact of an Enhanced Patient Care Intervention on Viral Suppression Among Patients Living With HIV in Kenya
Bibliographic record
Abstract
BACKGROUND: Effective patient-centered interventions are needed to promote patient engagement in HIV care. We assessed the impact of a patient-centered intervention referred to as enhanced patient care (EPC) on viral suppression among unsuppressed patients living with HIV in Kenya. SETTING: Two rural HIV clinics within the Academic Model Providing Access to Health care. METHODS: This was a 6-month pilot randomized control trial. The EPC intervention incorporated continuity of clinician-patient relationships, enhanced treatment dialog, and improved patients' clinic appointment scheduling. Provider-patient communication training was offered to all clinicians in the intervention site. We targeted 360 virally unsuppressed patients: (1) 240 in the intervention site with 120 randomly assigned to provider-patient communication (PPC) training + EPC and 120 to PPC training + standard of care (SOC) and (2) 120 in the control site receiving SOC. Logistic regression analysis was applied using R (version 3.6.3). RESULTS: A total of 328 patients were enrolled: 110 (92%) PPC training + EPC, 110 (92%) PPC training + SOC, and 108 (90%) SOC. Participants' mean age at baseline was 48 years (SD: 12.05 years). Viral suppression 6 months postintervention was 84.4% among those in PPC training + EPC, 83.7% in PPC training + SOC, and 64.4% in SOC ( P ≤ 0.001). Compared with participants in PPC training + EPC, those in SOC had lower odds of being virally suppressed 6 months postintervention (odds ratio = 0.36, 95% confidence interval: 0.18 to 0.72). CONCLUSIONS: PPC training may have had the greatest impact on patient viral suppression. Hence, adequate training and effective PPC implementation strategies are needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".