“We really need to surround people with care:” a qualitative examination of service providers’ perspectives on barriers to HIV care in Manitoba, Canada
Bibliographic record
Abstract
OBJECTIVE: To identify barriers to HIV care from the perspectives of HIV service providers in Manitoba (MB), Canada during the 2020-2022 period of the COVID-19 pandemic. METHODS: In this qualitative study, we conducted semi-structured interviews with HIV service providers between October 2022 and January 2023. Purposive sampling was used to include a cross-section of 27 providers (clinicians, nurses, social workers, pharmacists, program managers, and health education facilitators). The main themes explored in the interviews included: (1) provider roles and organization; (2) facilitators and barriers to HIV care; (3) harm reduction and sexually transmitted and blood-borne infections prevention practices; (4) impacts of the COVID-19 pandemic on HIV care and providers and (5) policies related to HIV care in Manitoba. RESULTS: Using a Social Ecological Model of Health framework, our analysis of service provider interviews identified barriers at four different levels: (1) structural level barriers, including limitations to public health and social support systems, geographic barriers, and policy inefficiencies; (2) socio-cultural/community level barriers, such as experiences of racism, stigma and discrimination leading to people living with HIV's (PLHIV) reduced trust in the health care system; (3) institutional level barriers, which describe how lack of primary care for PLHIV, limitations to the HIV care delivery model in Manitoba, and system capacity limitations have created missed opportunities for linkage to HIV care; and (4) intrapersonal barriers that reflect how the interaction of structural, socio-cultural, and institutional level barriers challenge providers' role performance and exacerbate risk of burnout and moral distress. CONCLUSIONS: Our findings demonstrate how multi-level barriers intersect to create challenges for both PLHIV and providers, limiting where and how people receive HIV care and impeding providers' ability to perform their roles and provide effective, consistent HIV care. Given the key role of HIV providers in facilitating care, structural, social/community, and institutional changes are needed, as is further research to examine structural causes of burnout to develop meaningful interventions that support service providers' mental health and well-being.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.040 | 0.016 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".