Setting the stage for Measurement-Based Care (MBC): Practical Lessons in the Implementation and Integration of MBC within Youth Wellness Hubs Ontario
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.097 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.022 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.012 | 0.026 |
| Research integrity | 0.012 | 0.031 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".