Visits to primary care physicians among persons who inject drugs at high risk of hepatitis <scp>C</scp> virus infection: room for improvement
Bibliographic record
Abstract
The role of primary care physicians (PCP) in hepatitis C virus (HCV) prevention is increasingly emphasized. Yet, little is known about the patterns of contacts with PCP among persons who inject drugs (PWID). We sought to assess the 6-month prevalence of PCP visiting among PWID at risk of HCV infection and to explore the associated factors. Baseline data were collected from HCV-seronegative PWID recruited in HEPCO, an observational Hepatitis Cohort study (2004-2011) in Montreal, Canada. An interviewer-administered questionnaire elicited information on socio-demographic factors, drug use patterns and healthcare services utilization. Blood samples were tested for HCV antibodies. Using the Gelberg-Andersen Behavioral Model, hierarchical logistic regression analyses were conducted to identify predisposing, need and enabling factors associated with PCP visiting. Of the 349 participants (mean age = 34; 80.8% male), 32.1% reported visiting a PCP. In the multivariate model, among predisposing factors, male gender [adjusted odds ratio (AOR) = 0.45 (0.25-0.83)], chronic homelessness [AOR = 0.08 (0.01-0.67)], cocaine injection [AOR = 0.46 (0.28-0.76)] and reporting greater illegal or semi-legal income [AOR = 0.48 (0.27-0.85)] were negatively associated with PCP visits. Markers of need were not associated with the outcome. Among enabling factors, contact with street nurses [AOR = 3.86 (1.49-9.90)] and food banks [AOR = 2.01 (1.20-3.37)] was positively associated with PCP visiting. Only one third of participating PWID reported a recent visit to a PCP. While a host of predisposing factors seems to hamper timely contacts with PCP among high-risk PWID, community-based support services may play an important role in initiating dialogue with primary healthcare services in this population.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".