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Enregistrement W4366463831 · doi:10.1111/hex.13763

Co‐building a training programme to facilitate patient, family and community partnership on research grants: A patient‐oriented research project

2023· article· en· W4366463831 sur OpenAlexafffundabout
Ingrid Nielssen, Sadia Ahmed, Sandra Zelinsky, Brian Dompe, Paul Fairie, Maria Santana

Notice bibliographique

RevueHealth Expectations · 2023
Typearticle
Langueen
DomaineHealth Professions
ThématiqueMental Health and Patient Involvement
Établissements canadiensUniversity of Calgary
Organismes subventionnairesCanadian Institutes of Health Research
Mots-clésGeneral partnershipTraining (meteorology)Community-based participatory researchMedical educationPsychologyMedicineNursingPolitical scienceSociologyParticipatory action researchGeography

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: Patient engagement in patient-oriented research (POR) is described as patients collaborating as active and equal research team members (patient research partners [PRPs]) on the health research projects and activities that matter to them. The Canadian Institutes of Health Research (CIHR), Canada's federal funding agency for health research, asks that patients be included as partners early, often and at as many stages of the health research process as possible. The objective of this POR project was to co-build an interactive, hands-on training programme that could support PRPs in understanding the processes, logistics and roles of CIHR grant funding applications. We also conducted a patient engagement evaluation, capturing the experiences of the PRPs in co-building the training programme. METHODS: This multiphased POR study included a Working Group of seven PRPs with diverse health and health research experiences and two staff members from the Patient Engagement Team. Seven Working Group sessions were held over the 3-month period from June to August 2021. The Working Group worked synchronously (meeting weekly online via Zoom) as well as asynchronously. A patient engagement evaluation was conducted after the conclusion of the Working Group sessions using a validated survey and semi-structured interviews. Survey data were analysed descriptively and interview data were analysed thematically. RESULTS: The Working Group co-built and co-delivered the training programme about the CIHR grant application process for PRPs and researchers in five webinars and workshops. For the evaluation of patient engagement within the Working Group, five out of seven PRPs completed the survey and four participated in interviews. From the survey, most PRPs agreed/strongly agreed to having communication and supports to engage in the Working Group. The main themes identified from the interviews were working together-communication and supports; motivations for joining and staying; challenges to contributing; and impact of the Working Group. CONCLUSION: This training programme supports and builds capacity for PRPs to understand the grant application process and offers ways by which they can highlight the unique experience and contribution they can bring to each project. Our co-build process presents an example and highlights the need for inclusive approaches, flexibility and individual thinking and application. PATIENT OR PUBLIC CONTRIBUTION: The objective of this project was to identify the aspects of the CIHR grant funding application that were elemental to having PRPs join grant funding applications and subsequently funded projects, in more active and meaningful roles, and then to co-build a training programme that could support PRPs to do so. We used the CIHR SPOR Patient Engagement Framework, and included time and trust, in our patient engagement approaches to building a mutually respectful and reciprocal co-learning space. Our Working Group included seven PRPs who contributed to the development of a training programme. We suggest that our patient engagement and partnership approaches, or elements of, could serve as a useful resource for co-building more PRP-centred learning programmes and tools going forward.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,009
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,066
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0090,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,004
Études des sciences et des technologies0,0140,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,004
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

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 Opus0,898
Tête enseignante GPT0,631
Écart entre enseignants0,267 · 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 tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
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

Citations7
Publié2023
Routes d'admission3
Résumé présentoui

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