How are patient partners involved in health service research? A scoping review of reviews
Notice bibliographique
Résumé
Including patients and next of kin as partners in research can help promote the development and dissemination of results that are inclusive, usable and relevant to health service settings. However, the impact of such involvement remains largely anecdotal, necessitating research to identify methods for achieving meaningful involvement. The aim was to examine how patient partners are involved in research across health service settings by addressing three objectives: (1) How are patient partners involved in the research process? (2) What is the impact of involving patient partners in research? (3) What defines effective patient partner involvement in research? We conducted a scoping review by searching five databases (Embase, Scopus, MEDLINE, CINAHL, PsycINFO) and grey literature. Published reviews within health service settings examining patient partner involvement were included. Protocol papers and reviews on patient involvement in treatment and care were excluded. The review adhered to Arksey and O’Malley’s methodological framework and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Checklist. A total of 124 reviews were included. Most reviews have been published after 2014, primarily from the United Kingdom, Canada and the United States of America. Patient partners were involved with consultation and collaboration approaches in different stages of the research process, including identifying and prioritising (n = 49), designing (n = 57), managing (n = 40), undertaking (n = 53) and disseminating (n = 51) and less in commissioning (n = 11), implementing (n = 6) and evaluating impact (n = 17). Impact reporting varied, with few reviews (n = 11) explicitly defining ‘impact’ and its related concepts. Sixteen key enablers for effective patient partner involvement were identified. The most reported enablers included partnerships built on trust and inclusive communication (n = 56), training and support for patient partners (n = 53), flexibility (n = 48) and adequate resources (n = 45). A significant gap exists in defining and measuring patient partner involvement. Adequate resources and training are essential for furthering trust-based, inclusive partnerships between researchers and patient partners. Future research should prioritise improving impact assessment, addressing power imbalances and refining best practices to enhance effective involvement. Two authors contributed with lived experience as patients and next of kin. Four patient partners were consulted about the results, one of whom coauthored this scoping review. Patients and next of kin are encouraged to participate in planning, conducting and evaluating research studies. This involvement can generate more diverse, usable and valid research results. However, what constitutes ‘good enough’ involvement has not been thoroughly investigated. We aim to describe how patients and next of kin can be involved in research by answering three main objectives: (1) How are patient partners and next of kin involved in research? (2) How does involvement change the research? (3) What makes patient involvement in research work well? We carried out a scoping review, a type of research that maps out what has been studied so far, to summarise how researchers report involving patient partners in their studies and how this involvement affects the quality of the studies. We looked at 124 reviews. Most of the reviews were published in the United Kingdom, Canada, and the United States of America. Patients and next of kin take part in different research, mostly by giving advice or working closely with researchers. However, most reviews did not define what impact or related terms mean. Most also pointed out that researchers do not report clearly how patient involvement makes a difference. Sixteen enablers were found to foster the effective involvement of patient partners. In short, good teamwork based on trust, clear communication, and enough funding, training, and support is key to making patient involvement work well. We suggest that future research should create better guidelines to support effective involvement and find ways to measure its impact.
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 | 0,075 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,000 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,006 |
| 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 ».