Digging Deep: Barriers to HIV Care Among Indigenous Women
Bibliographic record
Abstract
Indigenous women experience a disproportionate burden of ill health, including high rates of HIV. To reduce disparities in disease burden and health outcomes, identification of the barriers preventing access to health care is necessary. Identifying and discussing these barriers can assist service providers in the provision of care, influence policies for health and social well-being, and advance the discourse on equitable health care for Indigenous Peoples. Our research goal was to identify evidence-based, community-driven and asset-based solutions from the perspective of Indigenous women living with HIV. We also aimed to identify the role of the social determinants of health that influence the rates of HIV among Indigenous women. We used a combination of community-based participatory research methodology and Indigenous storytelling during 148 one-on-one interviews with HIV- and/or HCV-positive Indigenous women. Nine additional interviews were executed with healthcare professionals, health directors, and Knowledge Keepers and Elders. The interviews included qualitative, open-ended questions. We utilized NVivo for data analysis as well as Nanâtawihowin Âcimowina Kika-Môsahkinikêhk Papiskîci-Itascikêwin Astâcikowina (NAKPA), an Indigenous method for qualitative data analysis. Through the analysis, we identified nine barriers to care including expenses for daily living and health care-associated costs, time, access to computers and/or internet, transportation, childcare, homelessness and missed appointments, age, experiences with healthcare professionals and the health care system, and language. These barriers prevent access to and engagement in health care, leading to poor HIV related health outcomes. Healthcare providers have an essential role in identifying barriers to care, improving access to care in a patient-centered approach, and working to improve culturally safe practices.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".