Measuring impacts of patient and public involvement and engagement (PPIE): a narrative review synthesis of review evidence
Bibliographic record
Abstract
Patient and public involvement and engagement (PPIE), in its various forms, offers a wide range of potential benefits to research, health services and systems, and to those involved in this collaborative process. As PPIE has expanded over the years, so too have expectations regarding the evaluation of its effects and impacts. We conducted a narrative review synthesis of review articles around measurement of PPIE impact – conceptualising ‘impact’ to include any type of effect on people or processes, both proximate and longer-term. We searched PubMed, Cochrane Library of Systematic Reviews, and CINAHL electronic databases and conducted hand searches. Inclusion criteria comprised: public involvement, reporting impacts of public involvement, and using a review methodology. This yielded 27 review articles based on studies in the UK, US, Canada and Australia. We employed a three-part analysis process: 1) extracting all subcategories of impact reported into Excel (n = 37); 2) combining and categorising this list into primary and subcategories of impact based on thematic analysis; and 3) cross-checking these categories with the original review. Our review of reviews indicates that studies often do not report impacts of PPIE activities and when they do, they report a wide range, with little consistency across studies. We classified four broad types of PPIE impacts on: people (PPIE contributors, researchers, healthcare staff and policymakers), different phases of the research process, services and systems and on PPIE processes themselves. Across these categories, the most commonly documented impacts relate to impacts on PPIE collaborators, including individual empowerment and recovery, on researchers, improving their understanding of and collaboration with people typically excluded from research and on earlier phases of the research process. Studies reported both positive and negative impacts. Methodologically, previous evaluations of PPIE impact predominantly relied on retrospective self-reporting, with little triangulation from other data sources or prospective data collection over time. The impacts of PPIE appear to be under- and inconsistently reported. More robust evaluation of PPIE impact, drawing on the broad categories we present, offers opportunities for PPIE contributors, researchers and funders to better understand the effects of these investments. Where did we start? Patient and public involvement and engagement (PPIE) is a common way of making research more relevant to members of the public. The amount of PPIE that researchers do has increased in the last two decades, yet what the impact is of these activities is less clear. Recording impacts helps us keep track of how PPIE shapes people and research on this bigger scale. What did we do? We searched for academic review articles that mentioned impacts of PPIE. Out of 35,335, we identified how previous studies have defined and measured different types of impacts. What did we find? We identified four broad types of PPIE impacts on: people (PPIE contributors, researchers, healthcare staff and policymakers), different phases of the research process, services and systems and on the ways in which PPIE is done. Studies reported both positive and negative impacts. They measured change most often by asking researchers and PPIE contributors what they thought the impacts had been.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".