Caught between two worlds: mental health literacy and stigma among bicultural youth
Bibliographic record
Abstract
PURPOSE: Bicultural youths are at higher risk of mental health problems and are less likely to utilize mental health services, yet our knowledge of their mental health literacy and help-seeking behaviours remains limited. METHODS: To fill this gap, the current study explored bicultural youths' mental health literacy and stigma by conducting semi-structured interviews with 14 Canadian university students in 2021. RESULTS: Our analysis revealed that bicultural youths may be torn between two worlds: intergenerational tensions between participants assimilated into individualistic Canadian culture and their more collectivist parents meant that they had different cultural perceptions of mental health literacy and stigma. While being caught between these two worlds may be detrimental for bicultural youth, our results also suggested that a trans-cultural factor-celebrities' mental health journeys-may promote help-seeking behaviour across participants. Furthermore, our study speaks to the ways that unprecedented events such as the COVID-19 pandemic impact mental health literacy among bicultural youth. Our findings might be used by university mental health services to encourage help-seeking among bicultural students. CONCLUSION: The acculturation of mental health literacy, stigma, and associated intergenerational differences needs to be considered by university wellness services.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".