Social values for health technology assessment in Canada: a scoping review of hepatitis C screening, diagnosis and treatment
Bibliographic record
Abstract
BACKGROUND: Health care system decision makers face challenges in allocating resources for screening, diagnosis and treatment of hepatitis C. Approximately 240,000 individuals are infected with the hepatitis C virus (HCV) in Canada. Populations most affected by HCV include Indigenous people, people who inject drugs, immigrants and homeless or incarcerated populations as well as those born between 1946 and 1965. Curative but expensive drug regimens of novel direct acting antivirals (DAAs) are available. We aim to identify social values from academic literature for inclusion in health technology assessments. METHODS: We conducted a scoping review of academic literature to identify and analyze the social values and evidence-based recommendations for screening, diagnosis and treatment of HCV in Canada. After applying inclusion/exclusion criteria, we abstracted: type of intervention(s), population(s) affected, study location, screening methods, diagnostics and treatments. We then abstracted and applied qualitative codes for social values. We extracted social value statements and clustered them into one of 4 categories: (1) equity and justice, (2) duty to provide care, (3) maximization of population benefit, and (4) individual versus community interests. RESULTS: One hundred and eighteen articles met our inclusion criteria on screening, diagnosis and treatment of HCV in Canada. Of these, 54 (45.8%) discussed screening, 4 (3.4%) discussed diagnosis and 60 (50.8%) discussed treatment options. Most articles discussed the general population and other non-vulnerable populations. Articles that discussed vulnerable populations focused on people who inject drugs. We coded 1243 statements, most of which fell into the social value categories of equity and justice, duty to provide care and maximization of population benefit. CONCLUSION: The academic literature identified an expanded set of social values to be taken into account by resource allocation decision makers in financially constrained environments. In the context of hepatitis C, authors called for greater consideration of equity and justice and the duty to provide care in making evidence-based recommendations for screening, diagnosis and treatment for different populations and in different settings that also account for individual and community interests.
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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 | 0.072 | 0.255 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.043 | 0.064 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".