Ethical Challenges Related to Patient Involvement in Health Technology Assessment
Bibliographic record
Abstract
Including information and values from patients in HTA has the potential to improve both the process and outcomes of health technology policy decisions. Accordingly, funding and structural incentives to include patients in HTA activities have increased over the past several years. Unfortunately, these incentives have not yet been accompanied by a corresponding increase in resources, time, or commitment to responsiveness. In this Perspectives piece, we reflect on our collective experiences participating in, conducting, and overseeing patient engagement activities within HTA to highlight the ethical challenges associated with this area of activity. While we remain committed to the idea that patient engagement activities strengthen the findings, relevance, and legitimacy of health technology policy, we are deeply concerned about the potential for these activities to do ethical harm. We use this analysis to call for action to introduce strong protections against ethical violations that may harm patients participating in HTA engagement activities.
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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 | 0.219 | 0.274 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.038 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".