Using an intersectionality lens to explore barriers and enablers to hepatitis C point-of-care testing: a qualitative study among people who inject drugs and service providers
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus (HCV) infection is a significant global health burden, particularly among people who inject drugs. Rapid point-of-care HCV testing has emerged as a promising approach to improve HCV detection and linkage to care in harm reduction organizations such as needle and syringe programs. The objective of this study was to use an intersectionality lens to explore the barriers and enablers to point-of-care HCV testing in a needle and syringe program. METHODS: A qualitative study was conducted using semi-structured interviews with clients (people who inject drugs) and service providers in a large community organization focused on the prevention of sexually transmitted and blood borne infections and harm reduction in Montreal, Canada. An intersectionality lens was used alongside the Theoretical Domains Framework to guide the formulation of research questions as well as data collection, analysis, and interpretation. RESULTS: We interviewed 27 participants (15 clients, 12 providers). For clients, four themes emerged: (1) understanding and perceptions of HCV testing, (2) the role of an accessible and inclusive environment, (3) the interplay of emotions and motivations in decision-making, and (4) the impact of intersectional stigma related to HCV, behaviors, and identities. For providers, five themes emerged: (1) knowledge, skills, and confidence for HCV testing, (2) professional roles and their intersection with identity and lived experience, (3) resources and integration of services, (4) social and emotional factors, and (5) behavioral regulation and incentives for HCV testing. Intersectional stigma amplified access, emotional and informational barriers to HCV care for clients. In contrast, identity and lived experience acted as powerful enablers for providers in the provision of HCV care. CONCLUSION: The application of an intersectionality lens provides a nuanced understanding of multilevel barriers and enablers to point-of-care HCV testing. Findings underscore the need for tailored strategies that address stigma, improve provider roles and communication, and foster an inclusive environment for equitable HCV care. Using an intersectionality lens in implementation research can offer valuable insights, guiding the design of equity-focused implementation strategies.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".