“I am a Black man in Canada! I wish I could say differently!”: exploring the impact of direct and indirect encounters with the police and welfare system on anxiety and depression among Black youth in Toronto, Canada
Bibliographic record
Abstract
Introduction: Recent studies point to differences in mental health outcomes among Black youth living in Canada, influenced by structural experiences of anti-Black racism. The influence of policing and encounters with the criminal justice and child welfare systems in Canada on Black youth mental health outcomes remains understudied, exacerbated by the minimal collection of race-based health data in the country. Methods: Based on an intersectional approach and using semi-structured interviews with twenty-four Black youth in the Greater Toronto Area, this study explores how direct and indirect encounters with the criminal justice and child welfare systems in Toronto influence anxiety and depression symptomatology among Black youth and their families. Results: We found that this population experienced significant psychosocial weathering and hypervigilance and physical insecurity, with adaptive capacity being eroded by a sense of disposability. Discussion: This study contributes new evidence to research on criminalization and racism in Canada and proposes a critical health approach to studying these issues by paying attention to the caregiving burden among Black families experiencing criminalization, as well as the influence of space and place in mitigating the health impact of police and welfare encounters.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".