BARRIERS AND FACILITATORS TO ACCESSING MENTAL HEALTH CARE BY ARABIC-SPEAKING NEWCOMER CHILDREN AND YOUTH IN HAMILTON, ONTARIO
Bibliographic record
Abstract
Background: Evidence suggests that refugee and immigrant children and youth are less likely to access needed mental health care. In most cases, settlement service providers may assist by connecting individuals with local resources to help with the transition and providing non-clinical mental health and wellbeing assistance. Few studies have examined the access to mental health care by newcomer children and youth, creating a knowledge gap in addressing the barriers and facilitators for accessing mental health services. This study aimed to explore the service providers’ perceptions of the barriers and facilitators to accessing mental health services for Arabic-speaking newcomer children and youth in Hamilton, Ontario. Methods: Data was collected using semi-structured key informant interviews with service providers (n=7) representing a variety of sectors. Data were analyzed using thematic analysis. Results: Six themes identified the data's most significant and pertinent aspects relative to my research question. The attitudes of Arabic-speaking newcomers toward mental health and mental well-being, the stigma around mental health, and trust-related issues were identified as three distinct individual factors that can function as barriers to seeking mental health care. Another theme emphasized the importance of the cultural competency and diversity of service providers. Lastly, two themes addressed health system-related variables that highlighted the gaps and challenges in the existing mental health care system for newcomers and the detrimental effects of the COVID-19 pandemic. Conclusion: Enhancing and enabling access to mental health care for all newcomer children and youth is essential for their current and future mental health and wellbeing. This study suggests a few recommendations and future directions for service providers, researchers, and decision-makers to promote newcomers’ access to mental health care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".