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Enregistrement W4390939783 · doi:10.5334/ijic.icic23305

Building Evaluation Capacity for Integrated Care: Lessons from an Embedded Researcher Program

2023· article· en· W4390939783 sur OpenAlexaffabout
Patrick Feng, Meghan McMahon, Angela Del Monte, Ross Baker

Notice bibliographique

RevueInternational Journal of Integrated Care · 2023
Typearticle
Langueen
DomaineHealth Professions
ThématiquePrimary Care and Health Outcomes
Établissements canadiensCanadian Institutes of Health ResearchOntario College of Art and DesignUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésGovernment (linguistics)Health careIntegrated careBusinessPublic relationsNursingMedical educationKnowledge managementMedicinePolitical scienceComputer science

Résumé

récupéré en direct d'OpenAlex

Introduction: Evaluation has an important role to play in supporting integrated care. By collecting data early and often, care teams can better learn and adapt as new models of care are implemented. While the benefits of evaluation are well known, organizations may lack the capacity to conduct these well. This is particularly true of smaller organizations, where research is often seen as a luxury rather than a ‘must have.’ This paper presents lessons from the OHT Impact Fellows, a training program that places postdoctoral fellows in healthcare organizations where they support implementation and evaluation of integrated care projects. Since its launch in 2021, the program has placed 22 fellows in health teams across Ontario, Canada. Drawing on the collective experience of these fellows, we offer suggestions on how to build evaluation capacity for integrated care. Background: Introduced by the provincial government in 2019, Ontario Health Teams (OHTs) are a new way of organizing care in Ontario. The primary goal of OHTs is to deliver care in a more integrated way, with care providers in different sectors (e.g., hospitals, primary care, home and community care) working as one coordinated team. To support their development, the government has funded several support programs, including the OHT Impact Fellows. Designed with input from researchers, funders (government), and knowledge users (clinicians, health leaders, and patients), this program provides on-the-ground support tailored to the needs of host OHTs. The Program: Each year, OHTs are invited to submit expressions of interest to host a research fellow. Soon afterwards, a call is issued for fellowship applicants. After a rigorous selection process, fellows are matched with a host OHT based on their mutual fit. This process ensures that the skills and interests of fellows match the needs of their host organizations. Fellows then spend one year embedded in an OHT, supporting evaluation within and learning across OHTs. Fellows are matched with a host and academic mentor and supported with ongoing training and professional development opportunities. At the end of their fellowship, participants provide detailed feedback on the program through a survey. Results: Based on this survey data, here are some learnings so far: 1. Fellows were seen as highly impactful in supporting local projects and building OHT capacity. They were seen as moderately impactful in supporting learning across OHTs. 2. Fellows were extremely productive, sharing knowledge through conference presentations (>30), technical reports (>20), and internal briefings (>90) in one year. 3. Many fellows worked on projects that engaged patients and caregivers, often using a co-design approach. Patient engagement and co-design are among the top topics for which fellows have requested additional training. 4. Building evaluation capacity is as much about culture as analytical ability. For some organizations, ‘evaluation’ is a scary word that needs to be demystified before it can be embraced. Next Steps: We hope to offer a third round of fellowships in 2023. Audience: This presentation will be of interest to clinician-scientists, health leaders, researchers, and others interested in evaluation and its use in integrated care settings.

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)0,195
score de la tête « metaresearch » (Gemma)0,185
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,195
Score d'incertitude au seuil0,993

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,1950,185
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0020,002
Études des sciences et des technologies0,0170,025
Communication savante0,0180,024
Science ouverte0,0080,041
Intégrité de la recherche0,0070,013
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 Opus0,256
Tête enseignante GPT0,562
Écart entre enseignants0,307 · 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
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

Citations1
Publié2023
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueInternational Journal of Integrated Care→Même sujetPrimary Care and Health Outcomes→Travaux en français237 207→