EVALUATION OF PATIENT AND PUBLIC INVOLVEMENT INITIATIVES IN HEALTH TECHNOLOGY ASSESSMENT: A SURVEY OF INTERNATIONAL AGENCIES
Bibliographic record
Abstract
OBJECTIVES: Although there is increased awareness of patient and public involvement (PPI) among health technology assessment (HTA) organizations, evaluations of PPI initiatives are relatively scarce. Our objective as members of Health Technology Assessment International's (HTAi's) Patient and Citizen Involvement Group (PCIG) was to advance understanding of the range of evaluation strategies adopted by HTA organizations and their potential usefulness. METHODS: In March 2016, a survey was sent to fifty-four HTA organizations through the International Network of Agencies for Health Technology Assessment (INAHTA) and contacts of members of HTAi's PCIG. Respondents were asked about their organizational structure; how patients and members of the public are involved; whether and how PPI initiatives have been evaluated, and, if so, which facilitators and challenges to evaluation were found and how results were used and disseminated. RESULTS: Fifteen (n = 15) programs from twelve countries responded (response rate 27.8 percent) that involved patients (14/15) and members of the public (10/15) in HTA activities. Seven programs evaluated their PPI activities, including participant satisfaction (5/7), process (5/7) and impact evaluations (4/7). Evaluation results were used to improve PPI activities, identify education and training needs, and direct strategic priorities. Facilitators and challenges revolved around the need for stakeholder buy-in, sufficient resources, senior leadership, and including patients in evaluations. CONCLUSIONS: A small but diverse set of HTA organizations evaluate their PPI activities using a range of strategies that reflect the range of rationales and approaches to PPI in HTA. It will be important for HTA organizations to draw on evaluation theories and methods.
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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 | 0.067 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".