A Social Network Analysis of Interagency Collaboration in the Mental Health Sector in Toronto, Canada: Service Providers’ Perspectives on Supporting Black Youth in Recovery
Bibliographic record
Abstract
Objective: There is minimal research on mental health service providers’ perspectives on existing interagency relationships, collaboration, and caring for Black youth. We sought to characterize interagency relationships between Mainstream and Black-focused mental health agencies in Ontario, and their impact on service providers’ ability to provide culturally safe care for Black youth.
 Research Design and Methods: A mixed methodological approach was used to explore service providers’ perspectives on the collaborative nature and presence of relationships, communication, and influence in the mental health sector. An online survey was disseminated to service providers in Toronto, Ontario between March 2020 and April 2020. 7 focus groups were conducted in Toronto between May 2020 and December 2020. Focus groups were conducted with Black youth, families/caregivers, community, and service providers. Quantitative data were analyzed using SPSS and visualized in the form of social network analysis using Gephi, while Nvivo 12 was utilized to identify and analyze themes from the qualitative data.
 Results: Findings included a lack of interagency collaboration, infrequent Mainstream-Black agency collaborations, Mainstream agencies’ disinterest in future partnerships with Black agencies, and feelings of unpreparedness amongst service providers to provide culturally safe care to Black youth or referrals.
 Conclusions: Collaboration between mainstream agencies is common in the sector. There is a paucity of Black-focused agencies and Black service providers in the existing social networks. A limited understanding of the systemic impacts anti-Black racism at the agency and service provider level might be impairing service providers’ ability to support the mental health recovery of Black youth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".