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Enregistrement W4407909572 · doi:10.1016/j.jclinepi.2025.111731

Patient- and public-driven health research: a model of co-leadership and partnership in research priority setting using a modified James Lind Alliance approach

2025· article· en· W4407909572 sur OpenAlexafffund
Wasifa Zarin, Sharmila Sreetharan, Amanda Doherty‐Kirby, Michael Scott, Elaine Zibrowski, Charlene Soobiah, Meghan J. Elliott, Sabrina Chaudhry, Safa Al-Khateeb, Clara Tam, Ba’ Pham, Sharon E. Straus, Andrea C. Tricco

Notice bibliographique

RevueJournal of Clinical Epidemiology · 2025
Typearticle
Langueen
DomaineHealth Professions
ThématiqueMental Health and Patient Involvement
Établissements canadiensPublic Health OntarioUniversity of CalgaryUniversity of TorontoLondon Health Sciences CentreInstitute for Work & HealthWestern UniversitySt. Michael's Hospital
Organismes subventionnairesCanadian Institutes of Health Research
Mots-clésAllianceGeneral partnershipPublic healthMedicinePublic relationsPolitical scienceNursing

Résumé

récupéré en direct d'OpenAlex

OBJECTIVES: To describe the Strategy for Patient-Oriented Research Evidence Alliance's methodological approach to systematically identify 23 high priority health research topics (three in 2021 and 20 in 2023) from patient partners (including caregivers) and members of the public across Canada and beyond. STUDY DESIGN AND SETTING: In 2021 and 2023, we collaborated with patient and public partners to co-design and co-conduct two priority setting initiatives. These initiatives involved a diverse group of patients, the public, clinicians, researchers, and health system decision-makers to systematically and collectively prioritize research topics based on their perceived importance and anticipated impact. We used a modified James Lind Alliance approach, where all participants were engaged as equal partners. The prioritization process consisted of the following steps: 1) identification and collection of research priorities from patients and the public; 2) summarizing the research priorities gathered; 3) conducting semistructured interviews (1-on-1 or focus groups depending on the number of submissions for each unique topic), conducting literature searches on each topic to identify relevant knowledge synthesis and appraising the quality of relevant evidence using the AMSTAR 2 (A MeaSurement Tool to Assess systematic Reviews) checklist, and preparing lay summaries (1-2 pages) for each unique topic using a predefined template cocreated with patient partners; 4) conducting a priority setting exercise with a multidisciplinary panel consisting of an interim priority setting rating questionnaire to score each topic based on nine questions, followed by a virtual workshop to reach consensus on the final rating and ranking of topics; and 5) facilitating research by funding selected topics and providing capacity-building support to research teams. We conducted a formal process evaluation of engagement, transparency, information management, and considerations of values and context in 2023. RESULTS: A total of 98 topics were received across two research priority setting initiatives. Approximately, half the submissions were made by individuals who identified as patients (2021: 45% [n = 5] and 2023: 52% [n = 45]), whereas the rest identified as caregivers or members of the public. Topics were spread across 26 health themes, with arthritis and osteoporosis (27% [n = 3]) being the most common theme in 2021 and quality of care (26% [n = 23]) in 2023. Twenty-three priorities from 98 topics submitted by patients and public were selected. A formal process evaluation in 2023 revealed 85% of the respondents who participated in the priority setting panel "strongly agreed" that their experience was valuable and they would participate again in a future initiative. The 23 prioritized projects are currently being co-led with the patient and public partner topic submitters and nominated research teams. CONCLUSION: Priority setting exercises successfully engaged a diverse group of interested parties, resulting in the identification of relevant and impactful research topics. The positive feedback from participants suggests that these exercises were well-received and that similar methodologies should be applied and refined in future efforts. PLAIN LANGUAGE SUMMARY: The Strategy for Patient-Oriented Research Evidence Alliance used a patient- and public-driven approach to set research priorities across different topics. They conducted two priority setting initiatives in 2021 and 2023, involving patients, the public, researchers, clinicians, and health system managers. This approach aimed to prioritize research topics based on their perceived importance and anticipated impact. A total of 98 topics were received across two research priority setting initiatives, with nearly half the submissions from patients and the other half from caregivers or members of the public. The topics covered 26 health themes, with arthritis and osteoporosis (2021) and quality of care (2023) being the most common themes. From these topics, the 23 highest priority topics were selected by a multidisciplinary priority setting panel. A formal process evaluation in 2023 revealed that 85% of the respondents who participated in the priority setting panel "strongly agreed" that their experience was valuable, and they would participate again in a future initiative. In conclusion, the priority setting exercises successfully engaged a diverse group of individuals and identified important research topics. The positive feedback suggests that this approach was well received and should be applied in future efforts.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,048
score de la tête « metaresearch » (Gemma)0,040
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,952
Score d'incertitude au seuil0,254

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0480,040
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0040,002
Études des sciences et des technologies0,0110,020
Communication savante0,0180,010
Science ouverte0,0040,015
Intégrité de la recherche0,0060,006
Charge utile insuffisante (le modèle a refusé de juger)0,0110,002

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.

Tête enseignante Opus0,969
Tête enseignante GPT0,721
Écart entre enseignants0,248 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeQualitatif
DomaineMéthodes
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2025
Routes d'admission2
Résumé présentnon

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