Measuring integration in “Integrated Youth Services” in Canada: Experiences with research and evaluation
Bibliographic record
Abstract
Background: There are various conceptual models of integration in youth health care and wellness but challenges exist when implementing these tools or surveys and putting them into practice. We will generate dialogue and share our experiences using these models in the context of embedded research and evaluation programs. The aim of these programs is to measure the extent to which services and systems are integrated, and linking this with an understanding of equity, access and outcomes for youth and caregivers. Audience: Integrated Youth Services (IYS) is a dynamic international movement that aims to build effective, youth-focused and integrated services for mental health, substance use, and related health and wellness needs. The audience for this Networking Session includes individuals and organizations working within the IYS service system as well as researchers and evaluators tackling issues of integration, embedded within health organizations. Engagement: There are several IYS organizations in Canada (e.g., Youth Wellness Hubs Ontario, Foundry, Huddle, Kickstand, Aire Ouverte), that offer integrated care experiences for young people and their caregivers delivered through province-wide networks of health and wellness centres. Our role as evaluators and researchers is to take a close look at organizational, functional, clinical and service levels and use evidence to support removing systemic barriers and create seamless care experiences for youth. Engaging youth, families, clinicians, and health care leaders is core to the design and delivery of the IYS model, and we are actively engaged in evaluating and researching our integration efforts with a view to continuous improvement and to sustaining change. Objectives: The goal of the session is to provide a space for researchers, evaluators, health system administrators and practitioners to come together to discuss common approaches to measuring and understanding integration within their organizations. Specifically, we aim to engage participants in sharing tools and practices, as well as discussing innovations and lessons learned from efforts in researching and evaluating integration, with a particular focus on approaches where research and evaluation are embedded within health service delivery contexts to close the gap between evidence, implementation and outcomes of care. Key question for international colleagues : What are your approaches to evaluating integration in embedded health system contexts? What tools and practices emerged? What worked and what didn’t? We will share insights from efforts in Canada to develop and implement research and evaluation tools to better understand how work and systems are integrated at IYS centres – and will invite international colleagues to share in dialogue to build on existing and emerging practices and innovations across organizations.
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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 | 0.067 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.013 |
| Science and technology studies | 0.026 | 0.011 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".