Proposed Standards for Implementing Stepped Care Models in Child and Youth Mental Health Service Systems: Results of a Pan‐Canadian Delphi Study
Bibliographic record
Abstract
BACKGROUND: Stepped care (SC) is being adopted in many countries as a framework for organising mental health care in diverse contexts. However, there is a lack of consistency in how SC has been defined and operationalised, limiting its effective application in practice. We describe the development of standards for implementing SC models in Canadian child and youth mental health (CYMH) contexts using a consensus-based approach. These standards are intended to support systems planners in creating cohesive CYMH systems across Canadian settings. METHODS: This study employed learning alliance and Delphi methodologies. A pan-Canadian multi-round Delphi process conducted in English and French was used to derive consensus on the inclusion and wording of individual clauses in the standard. Consensus with a threshold of 70% was set to determine the inclusion of individual clauses in the final standard. RESULTS: Sixty-eight individuals participated in the Delphi study (with a 76.48% retention rate) representing lived experience, service delivery, policy, and research expertise. Over three rounds, 29 clause items were revised and reduced to a final list of 24 clause items comprising SC implementation standards. Participant feedback indicated a desire for reduced ambiguity, considerations of the limitations of patient autonomy, and the need to clarify roles and responsibilities in system-wide activities. DISCUSSION: The results of this Delphi study represent the first multi-stakeholder, consensus-driven set of standards for implementing SC in CYMH settings across Canada. With these standards, we aspire to provide a blueprint for mental health systems advocacy and reform toward stronger, more coordinated CYMH systems.
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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 | 0.109 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".