Measuring impacts of patient and public involvement and engagement (PPIE): a narrative review synthesis of review evidence
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,033 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».