Hepatitis C virus infection in Saskatchewan First Nations communities: Challenges and innovations
Bibliographic record
Abstract
Hepatitis C virus (HCV) infection has become a major public health issue in Canada, and especially in Saskatchewan First Nations (FNs) communities. One of the challenges in eliminating hepatitis C in Canada is accessing hard-to-reach populations, such as FNs people living on reserves. In Canada, HCV is a notifiable disease but complete and timely surveillance of HCV data is not always possible in remote communities. In addition, national surveillance data are insufficient for determining the number of cases of hepatitis C among FNs populations, because many provinces do not collect information according to ethnicity. Statistics for FN communities are available federally through the First Nations and Inuit Health Branch (FNIHB) in partnership with the communities and the province. There are multiple factors associated with the high rates of HCV in FNs communities, including barriers in accessing preventive services, early diagnosis and treatment. These access issues are largely attributable to issues with geographical remoteness, transportation, education and awareness, and a health care system designed around urban health. New and innovative ways of delivering information and services, such as the mobile hepatitis C clinic (Liver Health Days) and the community-driven Sexually Transmitted Bloodborne Infections (STBBI) Know Your Status program, are proving invaluable in remote FNs communities. Extending these in-community and community-driven programs to other FNs communities and to the prison population could be invaluable in working towards the World Health Organization elimination goals of hepatitis C virus for all.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".