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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".