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Enregistrement W3116294225 · doi:10.1177/2054358120979255

Program Report: KidneyPRO, a Web-based Training Module for Patient Engagement in Kidney Research

2020· article· en· W3116294225 sur OpenAlexafffundabout
Leah Getchell, Eva Bernstein, Elisabeth Fowler, Linnea Franson, Marian Reich, Dwight Sparkes, Arlene Desjarlais, Sara Banai, Graham Pollock, Stephanie Lord-Fontaine, Craig Settee, Helen Robinson-Settee, Alicia Murdoch, Nicolás Fernández, Ruth Sapir‐Pichhadze

Notice bibliographique

RevueCanadian Journal of Kidney Health and Disease · 2020
Typearticle
Langueen
DomaineHealth Professions
ThématiqueMental Health and Patient Involvement
Établissements canadiensMcGill UniversityMcGill University Health CentreUniversité de MontréalKidney Foundation of Canada
Organismes subventionnairesCanadian Institutes of Health Research
Mots-clésMedicineMedical educationCollaborative networkKidney diseaseKnowledge managementComputer science

Résumé

récupéré en direct d'OpenAlex

PURPOSE OF REPORT: Over the recent years, there has been increasing support and traction for patient-oriented research (POR). Such an approach ensures that health research is focused on what matters most: improving outcomes for patients. Yet the realm of health research remains enigmatic for many patients in Canada who are not familiar with research terms and practices, highlighting the need for focused capacity-building efforts, including the development of novel educational tools to support patients to meaningfully engage in the research enterprise. The need for disease-specific training in POR was identified by the network dedicated to advancing patient-oriented kidney research in Canada, Canadians Seeking Solutions and Innovations to Overcome Chronic Kidney Disease (Can-SOLVE CKD), during the early years of the network's inception. In this report, we describe the development of KidneyPRO, an online learning module that orients patients and families to kidney research in Canada, and outlines ways to get involved. In line with the Patient Engagement framework of the Strategy for Patient Oriented Research, KidneyPRO was co-developed with the network's patient partners. SOURCES OF INFORMATION: The need for KidneyPRO was identified through a review of feedback from network participants of Canadian Institutes of Health Research's (CIHR) Foundations in Patient-Oriented Research Module 2-Health Research in Canada and a network-wide survey of Can-SOLVE CKD that was conducted in June 2017 and assessed training needs of key stakeholders. This 2017 survey ranked the need for tools providing introductory knowledge on Canadian kidney research as third in the network's top 5 capacity-building priorities. METHODS: At Can-SOLVE CKD, a dedicated multi-stakeholder team was formed from the Training & Mentorship Committee (the network's core infrastructure for POR capacity building) to determine the learning objectives, content, and user interface. The team consisted of 3 patient partners, Director of Research for the Kidney Foundation of Canada, a kidney clinician-scientist, the network's Patient Partnerships & Training Lead, Can-SOLVE CKD's Indigenous People's Engagement and Research Council Coordinator, and a project coordinator. With permission, content from CIHR's Foundations in Patient-Oriented Research, along with resources from the Kidney Foundation of Canada's research arm and network project teams, was used to form the basis of the tool. The working group adapted a DoTTI (Design and develOpment, Testing early iterations, Testing for Effectiveness, Integration, and implementation) framework and iteratively identified, created, and refined the content and user interface in consultation with the Training and Mentorship Committee and the Can-SOLVE CKD Patient Governance Circle. KEY FINDINGS: In this article, we describe the development, deployment, and evaluation of KidneyPRO, a web-based training module that helps patients understand general, patient-oriented, and kidney-specific research within Canada. KidneyPRO aims to support patient engagement in studies as partners and/or participants and empower them to take part in the research process in an active and meaningful way. It was co-designed and vetted by patients, which helps to ensure clear, useful content and a user-friendly interface. In addition, the module includes links to kidney research opportunities within the Can-SOLVE CKD Network and beyond. A literature review established that KidneyPRO fills an important gap in kidney-specific POR. Ongoing collection of website metrics and postcompletion surveys from users will be used to evaluate the effectiveness of the tool. LIMITATIONS: As an online tool, people who do not have adequate Internet access will not be able to use KidneyPRO. Currently, the tool is not compliant with all Web Content Accessibility Guidelines. Given how the landscape of patient partnership in research is constantly evolving, the content in KidneyPRO needs to be updated on a regular basis. IMPLICATIONS: Canadians with or at high risk of CKD now have access to an educational tool when seeking to engage as partners and/or participants in innovative kidney research.

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)0,007
score de la tête « metaresearch » (Gemma)0,018
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Méthodes · Signal consensuel: aucune
Score de désaccord entre enseignants0,993
Score d'incertitude au seuil0,492

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,018
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,001
Communication savante0,0020,002
Science ouverte0,0020,006
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,1470,037

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 Opus0,367
Tête enseignante GPT0,470
Écart entre enseignants0,103 · 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'étudeSans objet
DomaineMéthodes
GenreMéthodes

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

En bref

Citations9
Publié2020
Routes d'admission3
Résumé présentoui

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