Factors that contribute to the mental health of Black youth
Bibliographic record
Abstract
BACKGROUND: Black people are a growing population in Canada, but limited data are available on the factors that contribute to the mental health of Black youth in Canada. We sought to explore the factors that contribute to the mental health of Black youth in Alberta, Canada. METHODS: Using a youth-led participatory action research approach and an intersectional feminist theoretical perspective, we collected data from a diverse sample of Black youth (aged 16-30 yr) in Alberta. We conducted individual interviews and conversation cafés with Black youth. RESULTS: We completed 30 individual interviews and 4 conversation cafés with a total of 99 Black youth. Participants identified the dominant factors contributing to mental health problems as racial discrimination, the intergenerational gap in families, microaggression and stigma, academic expectations, financial stress, lack of identity, previous traumatic events and religion. They also identified factors that contributed positively to mental health, including a sense of accomplishment, openness about mental health, positive relationships, sense of community and spirituality. INTERPRETATION: Black youth in Alberta reported that anti-Black racism and intergenerational tensions are major factors that contribute to their mental health, which suggests a need to address anti-Black racism and ensure more equitable approaches for Black youth in Alberta.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".