PUBLIC AND PATIENT INVOLVEMENT IN HEALTH TECHNOLOGY ASSESSMENT: A FRAMEWORK FOR ACTION
Bibliographic record
Abstract
OBJECTIVE: As health technology assessment (HTA) organizations in Canada and around the world seek to involve the public and patients in their activities, frameworks to guide decisions about whom to involve, through which mechanisms, and at what stages of the HTA process have been lacking. The aim of this study was to describe the development and outputs of a comprehensive framework for involving the public and patients in a government agency's HTA process. METHODS: The framework was informed by a synthesis of international practice and published literature, a dialogue with local, national and international stakeholders, and the deliberations of a government agency's public engagement subcommittee in Ontario, Canada. RESULTS: The practice and literature synthesis failed to identify a single, optimal approach to involving the public and patients in HTA. Choice of methods should be considered in the context of each HTA stage, goals for incorporating societal and/or patient perspectives into the process, and relevant societal and/or patient values at stake. The resulting framework is structured around four actionable elements: (i) guiding principles and goals for public and patient involvement (PPI) in HTA, (ii) the establishment of a common language to support PPI efforts, (iii) a flexible array of PPI approaches, and (iv) on-going evaluation of PPI to inform adjustments over time. CONCLUSIONS: A public and patient involvement framework has been developed for implementation in a government agency's HTA process. Core elements of this framework may apply to other organizations responsible for HTA and health system quality improvement.
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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 | 0.378 | 0.166 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.034 | 0.124 |
| Scholarly communication | 0.043 | 0.040 |
| Open science | 0.014 | 0.041 |
| Research integrity | 0.041 | 0.032 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".