An Exploration on the Barriers to Accessing Mental Health Services Among South Asian Youth
Bibliographic record
Abstract
Research in Canada and the United States has found that South Asians routinely underutilize mental health services, however research on the barriers experienced by South Asians is limited, especially research regarding the experiences of South Asian youth (Inman et al., 2014; Islam et al., 2017; Karasz et al., 2016). Through a series of semi-structured interviews (N=20) with South Asian youth between the ages 16 to 27 in the Greater Toronto Area, the present study explores South Asian youths’ experiences accessing mental health services and the impact of COVID-19 on mental health and service use. Thematic analysis of interview data reveal that South Asian participants experience several barriers preventing effective mental health support. In particular, issues relating to the lack of diversity among service providers and difficulties building and maintaining rapport with service providers were described. Further, participants experienced mental health stigma from family members, the South Asian community, and health professionals which posed additional barriers to accessing services. These issues are further exacerbated by the COVID-19 pandemic as participants reported a worsening of mental health, increased substance use, and additional barriers associated with accessing remote mental health services. Results demonstrate a gap in current mental health services and shed light on individual and programmatic changes that can be implemented to better serve this population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".