Harnessing Embedded Research as a Strategy for Evidence-Informed Integrated Care Transformation - Lessons Learned from two Embedded Research Training Programs
Notice bibliographique
Résumé
Background: Embedded research is emerging as a key strategy to advance learning health systems and evidence-informed integrated care transformation. Embedded research aims to align research with the evidence needs of health system organizations and is intended to help inform organizational decision-making. To build the embedded research workforce and enhance capacity within health system organizations to engage with and draw value from embedded research, embedded research training programs have emerged. This study examines two of the largest embedded research training programs in Canada - the CIHR Health System Impact (HSI) Program and the Ontario Health Team (OHT) Impact Fellows Program - to distill key outcomes, impacts and promising practices for individuals seeking to maximize their value and contribution as an embedded researcher and for organizations seeking to harness embedded research as a strategy for evidence-informed integrated care transformation. Approach: A mixed-methods study design with multiple sources of data and grounded in the Canadian Health Services and Policy Research Alliance Informing Decision-Making Impact Framework was used to assess outcomes and impacts. Data from program documents and website review were used to describe and compare the two programs. Data from program reporting, including fellow and mentor reports and impact narratives, were used to examine outcomes and impacts. The program teams collaborated to review the outcomes and impacts and, from these, co-design a suite of promising practices for individuals and organizations to maximize the value of embedded research towards integrated care transformation. The draft promising practices were shared with integrated care-focused fellows, alum, and mentors in the programs for review and refinement. Their input was incorporated to finalize the promising practices. Results: The CIHR HSI Program and the OHT Impact Fellows Program share similar objectives and core design elements. The programs differ in several key factors, including geographic scope, eligibility criteria for the fellow and the embedding health system organization, duration of the embedded fellowship, prioritized focus areas, and size of cohort. Positive outcomes and impacts are observed in both programs at the level of the fellow (i.e., competency development), the mentor (i.e., expanded academic and system relationships, commitment to research, growth as a mentor), and the embedding health system organization (i.e., increased capacity for embedded research, evidence-informed projects). Several promising practices were identified that focus on developing key core competencies, ensuring a strong start to embedded research relationships, building a supportive culture for embedded research, and co-creating shared vision and goals for success between academic and health system organizations. Implications: The emerging evidence from the CIHR HSI and the OHT Impact Fellows programs suggests that embedded research is a promising tool to help advance evidence-informed integrated care. By co-creating embedded research promising practices with the programs participants, this study generates practical tips and considerations that can help other individuals and organizations optimize their embedded research impacts. This study also contributes to advancing several of the nine pillars of integrated care, including workforce capacity and capability (#5), system leadership (#6), and transparency of progress, results impact (#9).
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 | 0,279 | 0,172 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,010 | 0,026 |
| Communication savante | 0,021 | 0,020 |
| Science ouverte | 0,007 | 0,036 |
| Intégrité de la recherche | 0,006 | 0,016 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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.
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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».