Shortening and validation of the Patient Engagement In Research Scale (PEIRS) for measuring meaningful patient and family caregiver engagement
Bibliographic record
Abstract
Abstract Objective To shorten the Patient Engagement In Research Scale (PEIRS) to its most essential items and evaluate its measurement properties for assessing the degree of patients’ and family caregivers’ meaningful engagement as partners in research projects. Methods A prospective cross‐sectional web‐based survey in Canada and the USA, and also paper‐based in Canada. Participants were patients or family caregivers who had engaged in research projects within the last 3 years, were ≥17 years old, and communicated in English. Extensive psychometric analyses were conducted. Results 119 participants: 99 from Canada, 74 female, 51 aged 17‐35 years and 50 aged 36‐65 years, 60 had post‐secondary education, and 74 were Caucasian/white. The original 37‐item PEIRS was shortened to 22 items (PEIRS‐22), mainly because of low inter‐item correlations. PEIRS‐22 had a single dominant construct that accounted for 55% of explained variance. Analysis of PEIRS‐22 scores revealed the following: (1) acceptable floor and ceiling effects (<15%), (2) internal consistency (ordinal alpha = 0.96), (3) structural validity by fit to a Rasch measurement model, (4) construct validity by moderate correlations with the Public and Patient Engagement Evaluation Tool, (5) good test‐retest reliability (ICC2,1 = 0.86) and (6) interpretability demonstrated by significant differences among PEIRS‐22 scores across three levels of global meaningful engagement in research. Conclusions The shortened PEIRS is valid and reliable for assessing the degree of meaningful patient and family caregiver engagement in research. It enables standardized assessment of engagement in research across various contexts. Patient or public contribution A researcher‐initiated collaboration, patient partners contributed from study conception to manuscript write‐up.
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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 | 0.026 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".