Identifying barriers and enablers to opt-out hepatitis C virus screening in provincial prisons in Quebec, Canada: A multilevel, multi-theory informed qualitative study with correctional and healthcare professional stakeholders
Bibliographic record
Abstract
BACKGROUND: Diffuse implementation of hepatitis C virus (HCV) treatment is dependent on universal screening for HCV, but screening strategies are heterogenous across prisons in the province of Quebec (Canada). We sought to identify barriers and enablers to universal opt-out HCV screening and to describe the multisectoral decision-making processes related to HCV screening in Quebec provincial prisons. METHODS: A multilevel, multi-theory informed qualitative descriptive approach was used to conduct semi-structured interviews. Interview guides and analyses with correctional stakeholders were informed by the Consolidated Framework for Implementation Research (CFIR) and those with healthcare professionals (HCPs) were based on the Theoretical Domains Framework (TDF). Directed content analysis was used to identify domains within CFIR and TDF reflecting barriers and enablers to opt-out HCV screening. RESULTS: Sixteen interviews (correctional stakeholders: n = 8; HCPs: n = 8) were conducted in April-May 2021. Twelve CFIR constructs were identified as barriers, seven as enablers, and two as neutral factors for the implementation of opt-out HCV screening. Correctional stakeholders underscored the need for political will (construct: external policy and incentives), highlighted limited resources (construct: available resources), and expressed concerns for the lack of consideration of implementation issues (constructs: trialability, planning). Six TDF domains were identified among HCPs as relevant to the implementation of opt-out HCV screening: beliefs about consequences (mixed = enablers and barriers), environmental context and resources (barrier), social influences (barrier), optimism (mixed), emotions (mixed), and behavioural regulation (barrier). The decision-making processes vis-à-vis HCV care in Quebec correctional settings were found to be hierarchical and complex. CONCLUSIONS: The use of CFIR and TDF was helpful in identifying barriers and enablers to HCV screening at multiple levels for people incarcerated in Quebec provincial prisons. Going forward, several political, structural, and organizational factors should be addressed through the engagement of stakeholders and people with lived experience of incarceration.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".