The Politics of Child Health Technologies
Bibliographic record
Abstract
Health technology assessment (HTA) frameworks appraise the value of technologies – be they drugs, devices, procedures or services – to inform policy decision-making and resource allocation amongst alternatives within publicly funded health systems. The prevailing principles and metrics by which HTA is conducted were designed with adult health conditions and treatments in mind. The evidentiary and normative dimensions of HTA frameworks may have unique repercussions for drug policy and coverage decisions in children, but their relevance to child health has received almost no critical scrutiny in either academic or policy circles. Approaches to paediatric drug coverage approval and access currently lack child-specific data on social values and priorities, a core component of HTA in most countries with public drug funding programs, including Canada. This thesis presents a mixed methods study of social values relevant to child HTA and drug policymaking in publicly funded health systems, comprised of three original scientific contributions. The first of these is a critical interpretive synthesis (CIS) of the academic literature on the moral dimensions of child health and social policymaking across a range of disciplines and policy domains. The second is a grounded theory analysis of qualitative interviews with diverse health system stakeholders on the social values and health system factors relevant to child HTA and drug funding policy in Canada. The third is a stated preference survey of the general public that assesses societal preferences for health resource allocation to children as compared to adults, to generate evidence for priority setting on health technologies within Canada’s publicly funded health system. Together, these studies yield specific knowledge about the policy landscape for child health technologies in Canada, broad conceptual insights into the normative and methodological dimensions of child HTA, and a foundational understanding of the social values relevant to drug policy decisions for children.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| 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".