Evidence to Inform Policy Options for the Elimination of Hepatitis C Virus in First Nations Populations in Ontario
Bibliographic record
Abstract
In Canada, an estimated 220,000-246,000 people currently live with chronic hepatitis C viral (HCV) infection. To achieve the goals set by the World Health Organization (WHO) strategy to eliminate HCV infection by 2030, a mandate for action in the form of the Blueprint to Inform Hepatitis C Elimination Efforts in Canada was released. The Blueprint outlines what is necessary to achieve HCV elimination in Canada, emphasizing the need to inform specific knowledge gaps and calling on provinces/territories to develop action plans to meet the WHO targets for viral elimination. As a part of Canada’s commitment to the WHO strategy and the Blueprint, Canada has made First Nations-led approaches and First Nations populations a priority focus for national and provincial/territorial HCV strategies. Therefore, the purpose of this thesis is to generate evidence that can inform policy options for HCV elimination efforts in First Nations populations in Ontario. In Ontario, recent negotiations have allowed for partnerships between academic researchers and First Nations organizations to perform First Nations-directed research using administrative health data. In partnership with the Ontario First Nations HIV/AIDS Education Circle (OFNHAEC), we examined the health and economic impacts of HCV on Status First Nations populations in Ontario. The first study explored trends in the epidemiology and testing for HCV among a cohort of Status First Nations individuals across Ontario. The second study estimated the provincial publicly-funded healthcare costs associated with HCV among a cohort of Status First Nations individuals in Ontario. The third study uses the example of HCV screening within the emergency department setting to demonstrate how existing research can be used to perform economic evaluations of novel HCV case-finding approaches. With future work and using the evidence generated from this thesis, a similar approach can be used to evaluate First Nations-specific HCV interventions. Collectively, these studies inform research gaps necessary to reach the WHO goals for viral elimination and serve as reference in beginning to understand the impact of infection among First Nations individuals in Ontario. The data presented by this dissertation provides decision-makers with evidence that can inform the creation of HCV policy, prevention, and treatment efforts.
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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 | 0.030 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".