Supporting Quality Integrated Care for Adolescent Depression in Primary Care: A Learning System Approach
Bibliographic record
Abstract
Background: Quality integrated care, which involves primary care and mental health clinicians working together, can help identify and treat adolescent depression early. We explored systemic barriers to quality integrated care at the provincial level in Ontario, Canada using a learning system approach. Methods: Two Ontario Health Teams (OHTs), regional networks designed to support integrated care, completed the Practice Integration Profile (PIP) and participated in focus groups. Results: The OHTs had a median PIP score of 69 out of 100. Among the PIP domains, the lowest median score was case identification (50), and the highest one was workspace (100). The focus groups generated 180 statements mapped to the PIP domains. Workflow had the highest number of coded statements (59, 32.8%). Discussion: While the primary care practices included mental health clinicians on-site, the findings highlighted systemic barriers with adhering to the integrated care pathway for adolescent depression. These include limited access to mental health expertise for assessment and diagnosis, long wait times for treatment, and shortages of clinicians trained in evidence-based behavioral therapies. These challenges contributed to the reliance on antidepressants as the first line of treatment due to their accessibility rather than evidence-based guidelines. Conclusion: Primary care practices, within regional networks such as OHTs, can form learning systems to continuously identify the strategies needed to support quality integrated care for adolescent depression based on real-world data.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".