Reimagining mental health care for newcomer children and families: a qualitative framework analysis of service provider perspectives
Bibliographic record
Abstract
BACKGROUND: Persistent disparities in access to mental health care for refugee and immigrant children and their families pose unique challenges to policy and practice. This study examined service provider perspectives on the barriers and opportunities for improving mental health supports for newcomer children and families in Canada. METHODS: Semi-structured individual and group interviews were conducted with 33 leadership and frontline staff from 14 organizations in the health, education, settlement, and social service sectors in Hamilton, Ontario. Interview data were analyzed using the framework method. RESULTS: Participants described barriers at the systems, provider, and individual and family levels that prevented newcomer families from accessing and benefiting from mental health supports. Structural barriers included inadequate services and funding, complexity of systems, cultural tensions, and, lack of prevention and early identification. Provider-level barriers included lack of representation, mental health knowledge and cultural competency, and staff shortages and burnout. Individual and family-level barriers included lack of mental health literacy, primacy of settlement needs, stigma, fear, and the high threshold for help-seeking. Participants' recommendations for "reimagining care" related to newcomer engagement, person- and family-centered care, cultural responsiveness, mental health promotion and prevention, workforce diversity and development, collaborative and integrated care, and knowledge generation and uptake. CONCLUSIONS: The intersection of structural, provider, and individual/family-level barriers reduce newcomer families' access to and effectiveness of mental health supports. Reducing disparities in mental health and access to care will require a paradigm shift in the way that mental health care is conceptualized and delivered to newcomer children and families.
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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 | 0.024 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".