Involving patient in the early stages of health technology assessment (HTA): a study protocol
Bibliographic record
Abstract
BACKGROUND: Public and patient involvement in the different stages of the health technology assessment (HTA) process is increasingly encouraged. The selection of topics for assessment, which includes identifying and prioritizing HTA questions, is a constant challenge for HTA agencies because the number of technologies requiring an assessment exceeds the resources available. Public and patient involvement in these early stages of HTA could make assessments more relevant and acceptable to them. Involving them in the development of the assessment plan is also crucial to optimize their influence and impact on HTA research. The project objectives are: 1) setting up interventions to promote patient participation in three stages of the HTA process: identification of HTA topics, prioritization, and development of the assessment plan of the topic prioritized; and 2) assessing the impact of patient participation on the relevance of the topics suggested, the prioritization process, and the assessment plan from the point of view of patients and other groups involved in HTA. METHODS: Patients and their representatives living in the catchment area of the HTA Roundtable of Université Laval's Integrated University Health Network (covering six health regions of the Province of Quebec, Canada) will be involved in the following HTA activities: 1) identification of potential HTA topics in the field of cancer; 2) revision of vignettes developed to inform the prioritization of topics; 3) participation in deliberation sessions for prioritizing HTA topics; and 4) development of the assessment plan of the topic prioritized. The research team will coordinate the implementation of these activities and will evaluate the process and outcomes of patient involvement through semi-structured interviews with representatives of the different stakeholder groups, structured observations, and document analysis, mainly involving the comparison of votes and topics suggested by various stakeholder groups. DISCUSSION: This project is designed as an integrated approach to knowledge translation and will be conducted through a close collaboration between researchers and knowledge users at all stages of the project. In response to the needs expressed by HTA producers, the knowledge produced will be directly useful in guiding practices regarding patient involvement in the early phases of 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 | 0.129 | 0.081 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.049 | 0.016 |
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".