What does meaningful look like? A qualitative study of patient engagement at the Pan-Canadian Oncology Drug Review: perspectives of reviewers and payers
Bibliographic record
Abstract
Objectives While there is wide support for patient engagement in health technology assessment, determining what constitutes meaningful (as opposed to tokenistic) engagement is complex. This paper explores reviewer and payer perceptions of what constitutes meaningful patient engagement in the Pan-Canadian Oncology Drug Review process. Methods Qualitative interview study comprising 24 semi-structured telephone interviews. A qualitative descriptive approach, employing the technique of constant comparison, was used to produce a thematic analysis. Results Submissions from patient advocacy groups were seen as meaningful when they provided information unavailable from other sources. This included information not collected in clinical trials, information relevant to clinical trade-offs and information about aspects of lived experience such as geographic differences and patient and carer priorities. In contrast, patient submissions that relied on emotional appeals or lacked transparency about their own methods were seen as detracting from the meaningfulness of patient engagement by conflating health technology assessment with other functions of patient advocacy groups such as fundraising or public awareness campaigns, and by failing to provide credible information relevant to deliberations. Conclusions This study suggests that misalignment of stakeholder expectations remains an issue even for a well-regarded health technology assessment process that has promoted patient engagement since its inception. Support for the technical capacity of patient groups to participate in health technology assessment is necessary but not sufficient to address this issue fully. There is a fundamental tension between the evidence-based nature of health technology assessment and the experientially oriented culture of patient advocacy. Divergent notions of what constitutes evidence and how it should be used must also be addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.085 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.027 | 0.023 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| 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".