A collaborative approach to hepatitis C testing in two First Nations communities of northwest Ontario
Bibliographic record
Abstract
Background: Two remote First Nations communities each collaborated with an urban-based liver clinic to organize wide-spread testing, followed by linkage to care for hepatitis C virus (HCV). Method: Involvement of community members was central to planning and conduct of the programs. Samples were obtained using dry blood spot cards (DBS). A week-long pilot study in Community 1 investigated the effectiveness of the program, using DBS. Community 2, being larger, more remote, and known to be endemic for HCV was more challenging. Three-week-long testing drives plus a stand-alone testing day were used to collect samples over 5 months. Public Health Agency (PHAC)'s National Laboratory for HIV Reference Services (NLHRS) received and tested the DBS samples for HCV and other blood-borne infections. Outcomes were measured by number of people tested, the quality of the tests, and community members' satisfaction with the program and retained knowledge about HCV, based on interviews. Results: In Community 1, 226 people were tested for HCV over 4 days. 85% agreed to human immunodeficiency virus (HIV) testing as well. In Community 2, 484 people, one-half of the adult population, were tested. Surveys of participants showed food was the most significant draw, and Facebook the most effective way to inform people of the events. Interviews with staff and participants showed a high level of satisfaction. Conclusion: The results suggest this is an effective approach to testing for HCV in unusually challenging settings. Lessons from the program include the power of community involvement; and the effectiveness of a highly targeted health initiative when developed through collaboration.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".