Implementing a learning health system within an integrated youth service initiative -a real-world case study.
Notice bibliographique
Résumé
Background: Mental health and substance use disorders among youth continue to be of critical public health concern in Canada, and urgent action is required within the health system to better respond to the evolving health and wellness needs of youth. Foundry, an integrated youth services (IYS) initiative in British Columbia (BC), is piloting a learning health system (LHS) framework at two (out of 7) operational IYS centres. The goal of Foundry LHS is to optimize the flow of data to knowledge to practice, supporting rapid adaptation and the continuous improvement of service delivery, experiences, and wellness outcomes for youth. The objective of this study was to elucidate learnings from the pilot experience and identify the barriers and facilitators to LHS implementation in an IYS context. Approach: This study was co-designed with the IYS initiative and incorporated an integrated knowledge translation approach. Participants (n=2) were purposively recruited from the LHS implementation team and Foundry Central Office (i.e., the backbone organization supporting the community-based IYS centres). Individual interviews were conducted at the pre- and post-implementation stages of the pilot LHS project and followed a semi-structured guide to broadly capture knowledge and perceptions of the LHS framework, barrier and facilitators to its implementation, and the potential impact on the IYS and its community. Data were analyzed using directed content analysis, guided by the Theoretical Domains Framework and COM-B model. Overarching themes and subthemes were generated, and findings were further interpreted to determine the barriers and facilitators to implementing the LHS in an IYS context, as well as other key learnings from the pilot implementation process. Results: This real-world case study highlights the importance of ensuring that LHS values and structures are incorporated into the overall organizational culture of an IYS initiative. Key themes in participant perception of barriers to the pilot implementation included a lack of clarity, shared vision, change management, and meaningful engagement, leading to limited buy-in and motivation for change. Strong governance, leadership, operational support, and dedicated resourcing over time emerged as strong themes critical to the successful implementation of the LHS in an IYS context. Participants saw the pilot LHS project as an invaluable learning experience and perceived early barriers to be future facilitators of the LHS in this context. Importantly, participants felt that the IYS organizational values, people, and infrastructures were highly aligned with an LHS way of working. These ideas reflected major facilitators and were thought to have created optimism and motivation to continue the work of implementing an LHS across the initiative. Implications: Learnings from this project reveal important challenges and opportunities related to the implementation of an LHS within a dynamic and evolving IYS initiative. Results will be integral to informing the continued development, implementation, and scale-up of the LHS within the IYS context in BC and beyond.
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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 | 0,010 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,013 | 0,005 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,003 | 0,009 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».