Inclusive Mental Health: A Case Study of Newcomer Families’ Experiences of School-Based Mental Health
Bibliographic record
Abstract
It is estimated that approximately one in five children in Canada experiences significant mental health issues before the age of 18. This number is estimated to be even higher for immigrant and refugee children as they face numerous stressors related to the process of immigration and integration into a new country. Unfortunately, many children from immigrant and refugee backgrounds never receive mental health support. This is due to several factors including limited accessibility, fear of stigma, and a discrepancy between Western views of mental health and individuals’ own understanding of mental health. Whether children access mental health services (MHS) is often largely influenced by their family’s willingness to seek out and support mental health treatment for their children. However, newcomer families are often hesitant to engage with MHS, or when they do engage, friction may occur between families and mental health practitioners due to cultural differences. Therefore, understanding the experiences and perceptions of newcomer families related to MHS is critical for providing inclusive MHS in Canadian schools. This study used a constructivist framework, paired with case study methodology, to examine a single school in a large city in Canada, with the purpose of better understanding newcomer families’ experiences with school-based MHS. Data sources included interviews with newcomer students (n = 5), their parents (n = 4), and school personnel (n = 9), and publicly available school policy and practice documents (n = 16). Data collection and analysis occurred simultaneously, and the processes of open coding, analytical coding, memo-writing, and categorization were used to synthesize the data. Results from this study highlighted the different beliefs and understandings of MH held by newcomer youth, their parents, and school personnel, and the barriers that continue to exist for newcomer youth when accessing MHS in a school context. These barriers exist at the structural, provider, and individual/family level. However, structural issues were highlighted as most salient and problematic by both newcomers and school personnel. Based on these findings, implications and recommendations for practitioners, policymakers, and researchers are discussed.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".