Evaluating a patient engagement program within a national research network
Bibliographic record
Abstract
Abstract Background The Engagement of People with Lived Experience of Dementia (EPLED) is a program within the Canadian Consortium on Neurodegeneration in Aging (CCNA). The role of EPLED is to enable those with lived experience of dementia – persons with dementia and their care partners (i.e., friends, family and caregivers) – to be meaningfully and actively involved in the CCNA research process. The EPLED Advisory Group is composed of people with lived experience of dementia. Method The EPLED researchers, Advisory Group and program staff worked to develop an approach to evaluate EPLED activities. Evaluation will include capturing quantitative data on Advisory Group activities (e.g., number and nature of projects) as well as Advisory Group and CCNA perspectives on the engagement activity. Existing resources were used to identify key domains and Advisory Group members were invited to share their perspectives on the most relevant questions, including by voting on existing questions or suggesting their own. Ultimately, the Patient Engagement in Research Scale (PEIRS‐22) was selected to capture Advisory Group perspectives, but with two additional questions, from the full (37‐item) PEIRS and free‐text questions. EPLED also developed a short questionnaire for CCNA researchers to evaluate their experience working with Advisory Group members, including questions related to timing, objectives, engagement, collaboration and impact. Both questionnaires were transferred to Google Forms for online data collection. Result We will present the EPLED program evaluation plan and data from the first year of activities. We will describe the nature of the Advisory Group’s work as well as the perceptions from members and researchers. Conclusion Patient engagement in research is becoming more widespread and, for some research funding, an expectation. Our evaluation plan will be used to improve the EPLED program and plan future activities. Our results may also help other patient partners, researchers and research organisations to develop and implement their own activities.
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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 | 0.214 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".