Developing standards for the implementation of stepped care in child and youth mental health service settings: protocol for a multi-method, delphi-based study
Bibliographic record
Abstract
INTRODUCTION: Canadian youth mental health (YMH) systems have the potential to urgently tackle the mental health treatment gap currently impacting young people, and stepped care (SC) is one model that can address this need. The adoption of SC models can guide the development of better-connected YMH systems by simplifying transitions and care pathways. To do so requires robust standards that are co-created across stakeholder groups, including with lived experience experts, to ensure the effective implementation of SC models. METHODS AND ANALYSIS: This study aims to establish standards for implementing SC in Canadian child and YMH service settings by convening and developing a learning alliance (LA) of 65 individuals, translating guiding principles to standards via consensus methods (Delphi study), and operationalising and applying draft standards to three test ecosystems. Members of the LA will be recruited via snowball and purposive recruitment techniques to complete an e-Delphi study over three to four rounds until consensus is achieved. Participants will rank their agreement with including specific clause items in the final standard, and will be given opportunities to provide feedback and suggest revisions during each round. Comments will be analysed, scored and coded accordingly. Once consensus has been achieved, members of the LA will consider the application of these implementation standards in three settings that could benefit from SC. The protocol for this study was registered at Open Science Framework (https://doi.org/10.17605/OSF.IO/J5UNW). ETHICS AND DISSEMINATION: The protocol has been approved by the Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal-Mental Health and Neuroscience subcommittee. As part of the ethics approval, informed consent forms for all Delphi participants were created and distributed to participants ahead of the Delphi. This includes parental consent forms for all LA members participating in the study who are under the age of 18. On completion, the project will ultimately support the implementation of SC in diverse service systems and guide the development of a robust and connected mental health delivery system in Canada. The final standard will be shared with relevant government bodies and health planners and disseminated via academic and other platforms.
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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 | 0.218 | 0.164 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.045 | 0.011 |
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".