Commentary: Advocating for patient and public involvement and engagement in health economic evaluation
Bibliographic record
Abstract
BACKGROUND: Patient and public involvement in health economic evaluation is still relatively rare, compared to other areas of health and social care research. Developing stronger patient and public involvement in health economic evaluation will be important in the future because such evaluations can impact on the treatments and interventions that patients can access in routine care. MAIN TEXT: The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) is a reporting guideline for authors publishing health economic evaluations. We established an international group of public contributors who were involved in the update of the CHEERS 2022 reporting guidance, ensuring two items (areas of reporting) specifically about public involvement were included. In this commentary we focus on the development of a guide to support public involvement in reporting, a key suggestion made by the CHEERS 2022 Public Reference Group, who advocated for greater public involvement in health economic evaluation. This need for this guide was identified during the development of CHEERS 2022 when it became apparent that the language of health economic evaluation is complex and not always accessible, creating challenges for meaningful public involvement in key deliberation and discussion. We took the first step to more meaningful dialogue by creating a guide that patient organisations could use to support their members to become more involved in discussions about health economic evaluations. CONCLUSIONS: CHEERS 2022 provides a new direction for health economic evaluation, encouraging researchers to undertake and report their public involvement to build the evidence base for practice and may provide some reassurance to the public that their voice has played a part in evidence development. The CHEERS 2022 guide for patient representatives and patient organisations aims to support that endeavour by enabling deliberative discussions among patient organisations and their members. We recognise it is only a first step and further discussion is needed about the best ways to involve public contributors in health economic evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.129 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".