Building Evaluation Capacity for Integrated Care: Lessons from an Embedded Researcher Program
Notice bibliographique
Résumé
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,195 | 0,185 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,017 | 0,025 |
| Communication savante | 0,018 | 0,024 |
| Science ouverte | 0,008 | 0,041 |
| Intégrité de la recherche | 0,007 | 0,013 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,002 |
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.
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 source (Gemma direct ou Codex distillé), 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 ».