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Record W4412018141 · doi:10.1186/s40900-025-00748-6

Measuring impacts of patient and public involvement and engagement (PPIE): a narrative review synthesis of review evidence

2025· review· en· W4412018141 on OpenAlexaboutno aff
William Lammons, Anne L. Buffardi, Dalya Marks

Bibliographic record

VenueResearch Involvement and Engagement · 2025
Typereview
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
FundersDepartment of Health and Social CareNational Institute for Health and Care Research
KeywordsNarrativePublic engagementPsychologyPublic involvementHistoryPublic relationsPolitical scienceArtLiterature

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.033
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.392
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.717
GPT teacher head0.555
Teacher spread0.162 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations20
Published2025
Admission routes1
Has abstractyes

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