Acceptability and feasibility of integration of HIV care services into antenatal clinics in rural Kenya: A qualitative provider interview study
Bibliographic record
Abstract
The aim of this study was to explore the perspectives of healthcare providers on the advantages and disadvantages of integrating HIV care services, including highly active antiretroviral therapy (HAART), into antenatal care (ANC) clinics in rural Kenya. We conducted a qualitative study using in-depth interviews and thematic analysis; 36 healthcare providers from six health centres in Nyanza Province, Kenya participated. Effects on service providers included increased workload due to the incorporation of specialised HIV services into ANC clinics. Providers observed that integration results in decreased patient time spent at the health facility, increased efficiency and closer provider-patient relationships; all leading to increased patient satisfaction. Providers also said that women would be more likely to receive HAART and adhere to their treatment as a result of improved confidentiality and decreased stigma. However, a minority of providers noted that integration could result in longer appointment times for HIV-positive women at ANC clinics leading to inadvertent disclosure. Integration could lead to strengthened ANC, postpartum care, prevention of mother-to-child transmission and HIV care for women and their families. However, integration efforts need to take into account potential negative effects on ANC provider workload, disclosure and the quality of care.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".