A critical reflection on using the Patient Engagement In Research Scale (PEIRS) to evaluate patient and family partners' engagement in dementia research
Bibliographic record
Abstract
Introduction: Research involvement of people with lived experiences is increasing. Few tools are designed to evaluate their engagement in research. The Patient Engagement In Research Scale (PEIRS) is one of the few validated tools. Our team employed the PEIRS with patient and family partners with lived experiences of dementia every 6 months in a two-year telepresence robot project. This reflection paper reports our self-study on key learnings and proposes practical tips on using the PEIRS to evaluate patient and family partners' engagement in dementia research. It is the first to document a case using the PEIRS multiple times in a dementia research project. Methods: Guided by Rolfe et al.'s reflective model, we conducted three team reflective sessions to examine the team's experiences using the PEIRS to improve and evaluate patient and family partners' engagement in the research. We also reviewed our meeting notes and fieldnotes documented in the research journal. A reflexive thematic analysis was performed. Results: The team identified three key learnings: the values of using the PEIRS survey, the adaptations, and the factors influencing its implementation as an evaluation tool. Using the PEIRS provided significant benefits to the project, although some patient and family partners felt it was burdensome. The evaluation tool was enhanced with emojis and comment boxes based on suggestions from patient partners. The emojis introduced an element of fun, while the comment boxes allowed for personalized responses. Several factors influenced the PEIRS tool's effectiveness: the interviewer's identity, the confidentiality of responses and follow-ups, the timing and frequency of using the tool, and the presentation of the evaluations. These learnings led to the development of six practical tips,-"ENGAGE": Enjoyable and fun process, Never impose, Get prepared early, Adapt to the team's needs, Give people options, and Engage and reflect. Conclusion: With the emerging trend of including people with lived experiences in dementia research, there is a need for ongoing assessment of engagement from both patient and family partners and the research team strategies. Future research can further explore survey logistics, co-development of evaluation tools, and the use of tools with people living with dementia.
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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 | 0.090 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".