Learning strategies for laypeople to participate in health technology assessment: a scoping review
Bibliographic record
Abstract
OBJECTIVES: To provide an overview of learning strategies that health technology assessment (HTA) agencies use worldwide to educate laypeople about HTA. METHODS: A scoping review focused on learning strategies to educate laypeople about HTA using the Joanna Briggs Institute frameworks was conducted across databases and gray literature. The study reviewed qualitative, quantitative, and mixed-methods studies from four databases, including practice documents from the HTA and health organization websites. RESULTS: Fifteen studies were included in this review. The United Kingdom, Spain, and Canada mainly contributed to knowledge about educating laypeople in HTA. The main strategies employed were conference-like events, educational materials, training, and plain language. International HTA and health agencies developed courses, online training, and guidance materials to increase laypeople's participation in the HTA process. CONCLUSIONS: Efforts to improve public involvement in HTA focus on structured consultations, digital platforms, and capacity-building to enhance accessibility. Strategies like workshops and plain language aim to encourage lay participation, but challenges such as technical complexity and limited resources persist. Despite these challenges, incorporating patient perspectives has increased research relevance and public trust. Future studies should examine standardized frameworks for involvement, the impact of lay participation on policy, and solutions to barriers to a more equitable HTA process.
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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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".