Leveraging intermediaries’ skillsets to build implementation research and practice infrastructure: a qualitative case study
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Notice bibliographique
Résumé
BACKGROUND: Implementation science as a field is rapidly advancing. Moreover, implementation science plays a pivotal role in driving learning health systems to better realize health outcomes and impact for our communities. Yet, few reports detail the infrastructures that underpin embedding and managing implementation science activities. Furthermore, there is little guidance for designing these infrastructures (people-powered and/or inanimate supports essential for embedding implementation research questions in pilot, spread and scale initiatives) to address local research and practice needs. The Implementation Science Collaborative is one such infrastructure in Alberta, Canada that leverages existing expertise in implementation research and practice to facilitate embedded implementation research and increase the success rates of health innovation implementation for better health outcomes. This study sought to provide actionable recommendations for designing effective implementation infrastructure by examining the co-design of the Implementation Science Collaborative. METHODS: We conducted a longitudinal case study (2018-2021) of the Implementation Science Collaborative using document analysis and semi-structured interviews. We collected data from initiative planning and operations documents (n = 190) and semi-structured interviews with Implementation Science Collaborative members (n = 6). We applied the Large-Scale Change Driver Model as the analytical framework for qualitative analysis to generate insights into designing cross-sectoral implementation science infrastructure. RESULTS: Our analysis showed that infrastructure design and operationalization followed established principles of implementation planning and execution. Implementation intermediaries proved to be effective facilitators as they had the backgrounds required to guide co-design and implementation planning. Their political neutrality in the resulting infrastructure enabled them to address power imbalances among co-design partners. However, strong management leadership remained irreplaceable. Cross-sectoral leadership was essential in fostering and solidifying the partnerships required for supporting the local learning health system. CONCLUSION: The study findings highlight the effectiveness of a co-design approach, facilitated by intermediaries, in developing local implementation science infrastructure and management systems as a promising practice to implement for achieving outcomes. This approach enabled the creation of infrastructure designs that meet diverse user needs. However, co-design is a complex process that benefits from both intermediaries' skills and cross-sectoral leadership knowledge of the local learning health system.
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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 | 0,033 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,007 |
| Études des sciences et des technologies | 0,010 | 0,002 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| 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 découle