Measuring goal progress using the goal-based outcome (GBO) measure in Youth Wellness Hubs Ontario – an integrated youth mental health service
Bibliographic record
Abstract
Problem and Context. There is a growing body of evidence indicating that the prevalence of mental health difficulties, in particular anxiety and depression, is increasing among youth, and many young people in this age group (12-25 years) do not get adequate mental health support. Recently, a global movement to transform youth mental health services is underway with the establishment of integrated youth services (IYS) that offer youth-specific care and emphasize early intervention and prevention, developmentally and culturally- informed services, community engagement, evidence-based and evidence-informed interventions, and youth and family engagement. While outcome data has an important role in enhancing the effectiveness of care for youth mental health, having young people define for themselves the area they feel should be the focus of in an intervention are also common measures used in IYS settings. Goal-setting has been used in therapy for several decades, and agreement on goals between young person and their clinician is thought to be central to successfully building a good therapeutic relationship and improving outcomes. Who is it for? Service providers, researchers, clinicians, youth and families. Who did you involve and engage with? Participants were young people (>5000) who engaged with a Youth Wellness Hub service between April 2020 and October 2022. The data for this study comprised over 15,000 goals. Youth can set up to one, two or three goals at each visit using the Goal-Based Outcome (GBO) tool. Inductive thematic analysis was employed on the types of goals set by young people. What did you do? The aim of this study was to explore the type of service goals set by young people who are engaging with integrated mental health services. The study also looked at whether services facilitated progress in goals as set by young people. What results did you get? What impact did you have? The analysis identified a number of goal-related themes related to improving mental health, reducing substance use and other addictions, improving physical health, service navigation, and self-improvement. Data from presentation to last assessment will be reviewed to examine change in progress on goals over time. What is the learning for the international audience? Understanding the clinical utility of an idiographic measure like the GBO with young people accessing community-based services is critical because the tool allows for capturing aspects of young people’s lives that are of importance to them that may not be as easily captured on standardized outcome tools. What are the next steps? Future directions include continuous learning and evaluation of the GBO in integrated care settings and examining the association between goal progress and standardize outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".