Understanding Disparities: Mental Health and Neurodevelopmental Challenges, Supports and Barriers for Immigrant Families in Canada
Bibliographic record
Abstract
BACKGROUND: Neurodevelopmental disorders (NDDs) and mental health disorders (MH) present significant challenges to Canadian Children. While there is increased awareness, the NDD/MH service needs and barriers to service for immigrant children in Canada are unclear. Therefore, the present study explores NDD and MH problems and management among Canadian children compared to immigrant children. METHOD: = 7.4), 41.3% of whom were immigrants, completed the survey. Participants were asked to complete questionnaires related to mental health in general, child MH and NDD service needs, social support and use and barriers to accessing services. RESULTS: Results showed that immigrant participants reported significant underuse of child mental health services (1.5 times less use) despite a higher reported child need. Similarly, a higher frequency of children born to Canadian parents reported accessing NDD/MH assessment referrals compared to immigrant families. Parents of children referred for NDD/MH assessment also reported a higher prevalence of mood disorders and anxiety disorders. Furthermore, parents of children presenting with NDD/MH concerns overall reported a significantly higher impact of barriers to their child's education compared to parents whose children did not present with NDD/MH concerns. This effect was driven by Canadian parents of children with NDD/MH reporting increased barriers. CONCLUSIONS: These findings highlight the importance of considering cultural background in clinical approaches to MDD/MH services. There is a need to increase awareness and reduce stigma regarding service access. Furthermore, the findings reiterate the ongoing challenges families of children with NDD/MH challenges face in accessing support.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".