Collaborative approaches to crisis intervention: enhancing mental health support for newcomers
Bibliographic record
Abstract
Purpose This study aims to investigate the barriers faced by newcomers in Canada in accessing mental health and crisis intervention services, focusing on immigrant and racialized communities. It explores service models that integrate newcomer support organizations with mental health services to address these challenges. Design/methodology/approach This study uses a narrative review and environmental scan to synthesize existing scholarly findings on the barriers that newcomers and racialized immigrants face when accessing mental health services. It also explores effective strategies for culturally responsive mental health care and collaborative care models. Key findings from an environmental scan are presented, highlighting integrative models that connect immigrant and settlement organizations with mental health services. Findings The findings reveal significant gaps in crisis intervention services for newcomers, particularly in providing culturally responsive care. Many settlement organizations lack direct access to mental health services, and existing crisis services often fail to integrate language interpretation and cultural brokering. The environmental scan identified a lack of formal partnerships between settlement and crisis services, leading to fragmented care for newcomers. Originality/value This paper proposes the Building Equitable Newcomer Crisis Help (BENCH) program, a collaborative model developed to bridge the gap between settlement organizations and crisis intervention services, offering a culturally responsive and integrated approach to crisis care for newcomers.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".