Patient engagement for the development of equity-focused health technology assessment (HTA) recommendations: a case study of two Canadian HTA organizations
Bibliographic record
Abstract
BACKGROUND: Health technology assessment (HTA) is a form of policy analysis that informs decisions about funding and scaling up health technologies to improve health outcomes. An equity-focused HTA recommendation explicitly addresses the impact of health technologies on individuals disadvantaged in society because of specific health needs or social conditions. However, more evidence is needed on the relationships between patient engagement processes and the development of equity-focused HTA recommendations. OBJECTIVES: The objective of this study is to assess relationships between patient engagement processes and the development of equity-focused HTA recommendations. METHODS: We analyzed sixty HTA reports published between 2013 and 2021 from two Canadian organizations: Canada's Drug Agency and Ontario Health. RESULTS: Quantitative analysis of the HTA reports showed that direct patient engagement (odds ratio (OR): 3.85; 95 percent confidence interval (CI): 2.40-6.20) and consensus in decision-making (OR: 2.27; 95 percent CI: 1.35-3.84) were more likely to be associated with the development of equity-focused HTA recommendations than indirect patient engagement (OR: .26; 95 percent CI: .16-.41) and voting (OR: .44; 95 percent CI: .26-.73). CONCLUSION: The results can inform the development of patient engagement strategies in HTA. These findings have implications for practice, research, and policy. They provide valuable insights into HTA.
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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 | 0.016 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 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".