A Mixed Methods Study Examining Citizenship Among Youth With Mental Health Challenges
Bibliographic record
Abstract
Introduction: Multiple stakeholders have recently called for greater research on the barriers to citizenship and community belonging faced by people with mental health challenges. Citizenship has been defined as a person's access to the rights, roles, responsibilities, resources and relationships that help people feel a sense of belonging. Factors that may impact citizenship include financial precarity; intersecting forms of marginalization and oppression (e.g., racism); and the mental health care people receive. Research has yet to examine experiences of citizenship among youth with mental health challenges. To address this gap, this study will examine how youth experience citizenship; predictors of citizenship; how citizenship shapes recovery; and the degree to which youth are receiving citizenship-oriented care. Methods: The research objectives will be evaluated using a multiphase mixed methods research design. Quantitative data will be collected cross-sectionally using validated self-report questionnaires. Qualitative data will be collected using a hermeneutic phenomenological method using semi-structured interviews and focus groups. Analyses: Multiple stepwise regression analyses will be used to determine predictors of citizenship and if of citizenship predict recovery. Pearson correlations will be computed to determine the relationship between participants' perceived desire for, and receipt of citizenship-oriented care. Phenomenological analysis will be used to analyze qualitative data. Findings will then be mixed using a weaving method in the final paper discussion section. Conclusion: Findings from this study may support the development of citizenship-oriented healthcare in Canada.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".