Co-designing a participatory evaluation of older adult partner engagement in the mcmaster collaborative for health and aging
Bibliographic record
Abstract
Engagement of patients and the public in health research is crucial for ensuring research relevance and alignment with community needs. However, there is a lack of nuanced evaluations and examples that promote collaborative and reflective learning about partnerships with partners. The aim of this paper is to provide a case example of a participatory evaluation of the engagement of older adult partners in an aging-focused research centre. We outline our process of co-planning and implementing an evaluation of the McMaster Collaborative for Health and Aging's engagement strategy through the use of multiple methods, including a standardized tool and qualitative approaches. The team chose to explore and capture the engagement experiences and perspectives of the older adult partners within the Collaborative using a survey (the Public and Patient Engagement Evaluation Tool (PPEET)), an art-based method (photovoice), and a focus group. We present a brief summary of the findings but primarily focus this paper on the experiences of using each methodology and tool, with an emphasis on promoting dialogue on the benefits, limitations, and challenges. We reflect on the process of co-planning and the integration of both standardized tools and qualitative approaches to adopt a holistic approach to evaluating partnership within the Collaborative. Ultimately, this case example aims to provide practical guidance for other research groups navigating the complexities of partnership engagement and evaluation, thereby promoting meaningful partnerships in 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 | 0.152 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".