Making the case for citizenship-oriented mental healthcare for youth in Canada
Bibliographic record
Abstract
Purpose Varying stakeholders have highlighted how recovery-oriented mental health services such as youth mental health services have traditionally focused on supporting individual resources to promote recovery (e.g., agency) to the exclusion of addressing structural issues that influence recovery (e.g. poverty). One response to this criticism has been work helping people with mental health problems recover a sense of citizenship and sense of belonging in their communities. Work on citizenship has yet to influence youth mental healthcare in Canada’s provinces and territories. This paper aims to highlight ways that youth mental healthcare can better help youth recover a sense of citizenship. Design/methodology/approach The arguments described in this paper were established through discussion and consensus among authors based on clinical experience in youth mental health and an understanding of Canada’s healthcare policy landscape, including current best practices as well as guidelines for recovery-oriented care by the Mental Health Commission of Canada. Findings Here, this study proposes several recommendations that can help young with mental health problems recover their sense of citizenship at the social, systems and service levels. These include addressing the social determinants of health; developing a citizenship-based system of care; addressing identity-related disparities; employing youth community health workers within services; adapting and delivering citizenship-based interventions; and connecting youth in care to civic-oriented organizations. Originality/value This paper provides the first discussion of how the concept of citizenship can be applied to youth mental health in Canada in multiple ways. The authors hope that this work provides momentum for adopting policies and practices that can help youth in Canada recover a sense of citizenship following a mental health crisis.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".