Setting the stage for Measurement-Based Care (MBC): Practical Lessons in the Implementation and Integration of MBC within Youth Wellness Hubs Ontario
Notice bibliographique
Résumé
Problem and Context: Measurement-based care (MBC) involves the systematic administration of standardized measures and the use of results to drive clinical decision-making within a therapeutic relationship framework. It is an integral part of youth-centered care and a core standard of integrated youth mental health services. Despite the wealth of evidence for the benefits of MBC in supporting high quality care, the implementation of MBC into routine mental health care is rarely incorporated. Who is it for? Youth mental health problems are a growing concern in Canada and globally, causing significant distress, impairment, and negative adult outcomes when untreated. Despite evidence-based interventions, only a minority of youth access and receive adequate treatment, with most youth facing barriers to high quality and effective interventions. To address these gaps, integrated youth service (IYS) models are established globally for youth ages 12-25 and emphasize timely access, youth friendliness, and holistic care, integrate mental and physical health, substance use, education, employment, peer support, and navigation into ‘one-stop shops’. Youth Wellness Hubs Ontario (YWHO) is Ontario’s IYS network with 22 integrated service networks in more than 30 communities across the province. MBC is a core component of the YWHO model and is implemented in 22 sites. Who did you involve and engage with? A fundamental contribution of YWHO is the inclusion of meaningful youth and family engagement processes in service design, delivery, and evaluation. YWHO has youth and family advisory councils both at the provincial and local levels who contribute to planning and operations, including service planning, governance, training, evaluation, communications, and funding-related decision-making. What did you do? This presentation will present the strategies, challenges, solutions, and subsequent adaptations we used to implement MBC within integrated youth services. We will describe the lessons we learned as we confronted practical obstacles around implementing MBC into integrated care pathways. What results did you get? MBC lessons learned included: 1. Leadership, service provider, and youth engagement in MBC; selection of measures and when to use measures, integrated data platform and electronic health records, addressing data quality issues early on, preparing for a demanding process of change, establishing support and coaching mechanisms, adapting MBC to a virtual environment, and adapting MBC in non-dominant cultural contexts. What is the learning for the international audience? MBC has been shown to improve the quality of care and clinical outcomes but it also requires substantial commitment, time, resources, and change management that can make it difficult to implement well in integrated care settings. Despite these challenges, we provide some creative solutions and adaptations within our model of youth-centered care that will inform audiences of how to effectively use MBC to personalize care, and how best to use MBC to engage service providers and youth in monitoring and management of symptoms. What are the next steps? Future directions include continuous learning and evaluation of the implementation of MBC in integrated care settings and identifying the factors for successful implementation.
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 | 0,097 | 0,106 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,021 | 0,022 |
| Communication savante | 0,017 | 0,019 |
| Science ouverte | 0,012 | 0,026 |
| Intégrité de la recherche | 0,012 | 0,031 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,003 |
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 ».