Integration of HIV Care with Primary Health Care Services: Effect on Patient Satisfaction and Stigma in Rural Kenya
Bibliographic record
Abstract
HIV departments within Kenyan health facilities are usually better staffed and equipped than departments offering non-HIV services. Integration of HIV services into primary care may address this issue of skewed resource allocation. Between 2008 and 2010, we piloted a system of integrating HIV services into primary care in rural Kenya. Before integration, we conducted a survey among returning adults ≥18-year old attending the HIV clinic. We then integrated HIV and primary care services. Three and twelve months after integration, we administered the same questionnaires to a sample of returning adults attending the integrated clinic. Changes in patient responses were assessed using truncated linear regression and logistic regression. At 12 months after integration, respondents were more likely to be satisfied with reception services (adjusted odds ratio, aOR 2.71, 95% CI 1.32-5.56), HIV education (aOR 3.28, 95% CI 1.92-6.83), and wait time (aOR 1.97 95% CI 1.03-3.76). Men's comfort with receiving care at an integrated clinic did not change (aOR = 0.46 95% CI 0.06-3.86). Women were more likely to express discomfort after integration (aOR 3.37 95% CI 1.33-8.52). Integration of HIV services into primary care services was associated with significant increases in patient satisfaction in certain domains, with no negative effect on satisfaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".