Is this really equity? Engaging experts-by-experience in Learning Health Systems to generate meaningful change solutions
Notice bibliographique
Résumé
Background:Learning Health Systems (LHS) require rigorous integration of evidence, experience, expertise, and values as a formula to produce equitable and meaningful change solutions for population health and wellbeing. The Participatory Research to Action (PR2A) Framework can guide authentic engagement of experts-by-experience across the LHS learning cycle. Audience:To produce equity-driven solutions in a learning health system, a rigorous approach to authentic and meaningful engagement that values diverse experiences and expertise is needed. PR2A offers concrete steps engaging diverse groups of experts-by-experience to generate equitable solutions to complex health care issues. We invite anyone interested in authentic collaboration to produce meaningful, relevant, and transformative change through equity-driven solutions to attend our workshop. Approach: In this interactive workshop, attendees will learn about SE Research Centre PR2A Framework and its application for meaningful engagement of experts-by-experience to support equity-driven learning cycles and solutions (5 minutes). We will explore the framework iterative six-stage process and discuss how it has been used to integrate evidence, experience, expertise, and values as a formula for transformative change across a series of interconnected projects at a Canadian LHS (0 minutes). A brief overview of the projects will be given: ) identifying priority research questions on aging and mental health according to Canadians; 2) formalizing an expert-by-experience group dedicated to mobilizing knowledge of the priority questions; and 3) co-designing evidence-based solutions to two priority questions through participatory research. Challenges with equitably engaging diverse partners in these projects (e.g., increasing group diversity along linguistic and other dimensions; reliance on the research team to facilitate expert-by-experience groups) will be highlighted. For most of the workshop (35 minutes) attendees will be divided into small groups and work together to apply the PR2A framework using one of the priority unanswered questions on aging and mental health as a hypothetical research/innovation scenario. Attendees will be guided through the PR2A Framework first stage, discussing who needs to be involved in the LHS learning cycle, why, and to what end. Participants will be encouraged to think critically about how challenges to engaging experts-by-experience can be addressed in the research/innovation project design and implementation to improve inclusion, equity, and accessibility. Following the small group brainstorming, attendees will have an opportunity to share with the whole group their experiences and perceptions of PR2A applicability and usefulness to their work (0 minutes).This workshop will apply multiple methods of engagement to create an interactive environment and accommodate diverse learning preferences. Engagement approaches will include didactic presentation, small and large group facilitated discussion, and hands-on brainstorming with worksheets and creative materials (e.g., pen, paper, cut-outs). Outcomes:Following the small group discussion, one attendee from each group will share back with everyone their group proposed project, the priority question it addresses, and how PR2A informed equitable practices in the project planning/design. Attendees will also be given the chance to share their reflections and key take-aways with the large group, including knowledge they will apply to future projects and new ways of thinking about equity within LHS. Attendees will be able to take their worksheets and notes home with them, in addition to handouts about the PR2A Framework.
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 | 0,004 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».