An international cross-sectional study of dementia researchers’ own perspectives on patient and public involvement
Bibliographic record
Abstract
Patient and Public Involvement (PPI) can enhance the quality and relevance of health research. However, its implementation remains uneven across regions and research fields. This study explores how dementia and aging researchers perceive PPI's impact on their work. A cross-sectional survey was distributed to 392 researchers in Europe, Latin America, the USA, and Canada. Following multiple expert reviews, native speaker input, and pilot testing, the final survey was administered. Quantitative data were analyzed using descriptive statistics, group comparisons, and logistic regression models. Of 392 questionnaires, 91 were returned (23.2% response rate). Researchers in Europe, the USA, and Canada reported greater familiarity with PPI than those in Latin America. Notably, 45.1% of respondents selected "I prefer not to answer" when asked about PPI's role in their research; this was more common among Latin American participants, who also reported lower PPI knowledge. Logistic regression revealed that familiarity with PPI was associated with having more research experience, submitting more grant applications, better access to PPI groups, and using PPI to aid recruitment. These findings point to gaps in the awareness and application of PPI in aging and dementia research, with many researchers expressing uncertainty about its value. Regional disparities underscore the need for consistent, standardized approaches to PPI. Future efforts should focus on closing the gap between theoretical familiarity and active implementation to improve research quality and public engagement, particularly in under-resourced settings.
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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 | 0.038 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".