Patient engagement in research on dementia: Perceptions from researchers in a multidisciplinary research consortium
Bibliographic record
Abstract
Abstract Background Patient ‘engagement’ or ‘involvement’ in health research refers to including people with lived experience (i.e. individuals with personal experience of a health issue and their friends, family and caregivers) in the research process – not as study subjects, but as collaborators in planning, conducting and communicating research. Patient engagement is being advanced by researchers, funding organisations, advocacy groups and others on both moral and methodological grounds. Although patient engagement in research on dementia is not new, it is becoming more common. Still, there are unresolved questions of how to incorporate, evaluate and adapt engagement activities for different types of research. Method We are reporting results of a survey of researchers who are members of the Canadian Consortium on Neurodegeneration in Aging (CCNA), conducted to understand their perceptions of engaging people with lived experience of dementia in research. Result There were 84 responses (27% response rate). Respondents included biomedical (n=10; 12%), clinical (n=35; 42%), health services (n=27; 32%) and social/cultural/environmental/population health (n=12; 14%) researchers. Overall, almost all (n=78; 93%) agreed that people with lived experience of dementia can contribute meaningfully to the research process. Nearly two thirds of respondents (n=54; 64%) indicated their research already included engagement activities and the most frequently reported motivations were to increase the quality and relevance of the research and empower people who have lived experience of dementia. They reported engagement activities took place most often in the context of knowledge translation, priority setting and study recruitment. Of those who indicated no current engagement activities (n=30; 36%), most were interested in opportunities for engagement and, for those who were not interested, the most common reason reported was that it was not relevant to their area of research. Limitations to these results include the low response rate that likely introduced some selection bias; researchers with an interest in engagement may have been more likely to respond. Conclusion Many CCNA researchers working in the area of dementia are engaging people with lived experience in their research. These data will be used to describe how researcher knowledge, attitudes and activities differ according to type of research.
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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 | 0.134 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".