‘The problem is small enough, the problem is big enough’: a qualitative study of health technology assessment and public policy on drug funding decisions for children
Bibliographic record
Abstract
BACKGROUND: Public policy approaches to funding paediatric medicines in developed public health systems remain understudied. Current approaches to HTA present a variety of conceptual, methodological and practical problems in the context of child health. This study explores the technical and sociopolitical determinants of public funding decisions on paediatric drugs, through the analysis of interviews with stakeholders involved in or impacted by HTA for child health technologies at the provincial and national levels in Canada. METHODS: We undertook in-depth interviews with a purposive sample (n = 22) of stakeholders involved with or affected by drug funding decisions for children at the provincial (Ontario) and national levels in Canada. Grounded theory methods were employed to guide data collection and analysis. Theory on 'technology-as-policy' and the sociopolitics of health technologies served as sensitizing concepts for inductive data coding and analysis. Emergent themes informed the development of conceptual and practical insights on social values and system dynamics related to child HTA, of relevance to public policymaking on the coverage of health technologies for children in Canada. RESULTS: Participant reflection on the normative and systems dimensions of drug funding for children formed two broad categories: HTA paradigms and sociopolitical context. Our analysis revealed notable differences of context and substance related to child health technology production, evaluation and use. These differences spanned the major phases of HTA (from assembly to assessment to integration) and the surrounding sociopolitical milieu (from markets to governance to politics). Careful analysis of these differences sets in relief a number of substantive and procedural shortcomings of current HTA paradigms in respect of child health. Our findings suggest a need to rethink how HTA is structured and operationalized for child health technologies. CONCLUSIONS: Current approaches to health technology assessment are not well calibrated to the realities of child health and illness. Our study presents a nuanced and contextually grounded analysis of concepts instrumental to drug funding decisions for children. The insights generated are directly applicable to the Canadian and Ontario contexts, but also yield fundamental knowledge about HTA for children that are germane to drug policy in other health systems.
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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 | 0.032 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.027 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".