“Getting to the Table”: Changing Ideas about Public and Patient Involvement in Canadian Drug Assessment
Bibliographic record
Abstract
CONTEXT: Involving patients and the public in health policy may contribute to legitimacy and accountability. However, tensions may arise between paradigms of scientific-evidence-based decision making and new ideas valuing inclusivity and patient experience when evaluating and allocating health resources. This article asks whether 10 years of experience with public and patient involvement in Canadian drug assessment has affected participants' ideas about how it works. METHODS: The author surveyed the ideas of participants in the drug assessment process (members of expert committees, officials, and patient groups) as described in reports and hearings in 2005, 2007, and 2012 and conducted interviews in 2014 and 2016. FINDINGS: The author found some consensus across groups of participants regarding the broad goals of health technology assessment (HTA) and the validity of some form of public and patient involvement. There were also important areas of disagreement and uncertainty about how public and patient involvement should be used in drug assessment and how much impact it has on deliberations and recommendations. Overall, uncertainly about the specific role for public and patient involvement in HTA limits the potential for ideational change among participants. CONCLUSIONS: These findings have implications for evaluation of public and patient involvement, the way we understand ideational change, and practical questions of communicating health resource decisions.
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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 | 0.087 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.067 | 0.088 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.013 | 0.024 |
| 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".