A Qualitative Study of Barriers to Enrollment into Free HIV Care: Perspectives of Never-in-Care HIV-Positive Patients and Providers in Rakai, Uganda
Bibliographic record
Abstract
BACKGROUND: Early entry into HIV care is low in Sub-Saharan Africa. In Rakai, about a third (31.5%) of HIV-positive clients who knew their serostatus did not enroll into free care services. This qualitative study explored barriers to entry into care from HIV-positive clients who had never enrolled in care and HIV care providers. METHODS: We conducted 48 in-depth interviews among HIV-infected individuals aged 15-49 years, who had not entered care within six months of result receipt and referral for free care. Key-informant interviews were conducted with 12 providers. Interviews were audio-recorded and transcripts subjected to thematic content analysis based on the health belief model. RESULTS: Barriers to using HIV care included fear of stigma and HIV disclosure, women's lack of support from male partners, demanding work schedules, and high transport costs. Programmatic barriers included fear of antiretroviral drug side effects, long waiting and travel times, and inadequate staff respect for patients. Denial of HIV status, belief in spiritual healing, and absence of AIDS symptoms were also barriers. CONCLUSION: Targeted interventions to combat stigma, strengthen couple counseling and health education programs, address gender inequalities, and implement patient-friendly and flexible clinic service hours are needed to address barriers to HIV 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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".