Community-Based Infectious Disease Clinics:A Tool Of Engagement For VulnerablePopulations
Bibliographic record
Abstract
Background: Hepatitis C virus (HCV) infection is a major international public health concern, particularly among people who inject drugs (PWID). The Downtown East Side of Vancouver (DTES) is known for a high prevalence of HCV infection. Despite available services, significant numbers of patients remain undiagnosed or lack access to potentially curative therapy. As such, there is a need to develop innovative means of recruitment into care and to assess the interest to seek care. Methods: Community pop-up clinics (CPCs) were held at several DTES sites (including InSite, the first supervised injection facility in North America). HCV point-of-care testing was offered. Participants were also offered the opportunity to complete a survey to collect demographic information as well as data related to their knowledge about HCV treatment and readiness to receive it. A $10 incentive was offered for participation. Results: Since the inception of the program in March 2013, 1743 individuals have been tested, of which 1125 individuals completed the survey (implemented in January 2014), with 568 (33%) infected with HCV. About 45% were unaware of a cure for this infection, but a vast majority (over 80%) would consider HCV treatment for it, if it was offered. Conclusions: Despite the existence of a cure for HCV infection, several barriers to HCV treatment have been identified, particularly in vulnerable populations. These barriers can exist on the levels of the patient, healthcare provider, and the health care system as a whole. There is a significant gap in HCV treatment knowledge, but a general willingness to receive care. Innovative low-threshold programs must be developed to engage those individuals in care.
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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 | 0.041 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".