Health System Features That Enhance Access to Comprehensive Primary Care for Women Living with HIV in High-Income Settings: A Systematic Mixed Studies Review
Bibliographic record
Abstract
Women living with HIV in high-income settings continue to experience modifiable barriers to care. We sought to determine the features of care that facilitate access to comprehensive primary care, inclusive of HIV, comorbidity, and sexual and reproductive healthcare. Using a systematic mixed studies review design, we reviewed qualitative, mixed methods, and quantitative studies identified in Ovid MEDLINE, EMBASE, and CINAHL databases (January 2000 to August 2017). Eligibility criteria included women living with HIV; high-income countries; primary care; and healthcare accessibility. We performed a thematic synthesis using NVivo. After screening 3466 records, we retained 44 articles and identified 13 themes. Drawing on a social-ecological framework on engagement in HIV care, we situated the themes across three levels of the healthcare system: care providers, clinical care environments, and social and institutional factors. At the care provider level, features enhancing access to comprehensive primary care included positive patient-provider relationships and availability of peer support, case managers, and/or nurse navigators. Within clinical care environments, facilitators to care were appointment reminder systems, nonidentifying clinic signs, women and family spaces, transportation services, and coordination of care to meet women's HIV, comorbidity, and sexual and reproductive healthcare needs. Finally, social and institutional factors included healthcare insurance, patient and physician education, and dispelling HIV-related stigma. This review highlights several features of care that are particularly relevant to the care-seeking experience of women living with HIV. Improving their health through comprehensive care requires a variety of strategies at the provider, clinic, and greater social and institutional levels.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".