Building flexibility and managing complexity in community mental health: lessons learned in a large urban centre
Bibliographic record
Abstract
BACKGROUND: Across many jurisdictions, adults with complex mental health and social needs face challenges accessing appropriate supports due to system fragmentation and strict eligibility criteria of existing services. To support this underserviced population, Toronto's local health authority launched two novel community mental health models in 2014, inspired by Flexible Assertive Community Team principles. This study explores service user and provider perspectives on the acceptability of these services, and lessons learned during early implementation. METHODS: We purposively sampled 49 stakeholders (staff, physicians, service users, health systems stakeholders) and conducted 17 semi-structured qualitative interviews and 5 focus groups between October 23, 2014 and March 2, 2015, exploring stakeholder perspectives on the newly launched team based models, as well as activities and strategies employed to support early implementation. Interviews and focus groups were audio recorded, transcribed verbatim and analyzed using thematic analysis. RESULTS: Findings revealed wide-ranging endorsement for the two team-based models' success in engaging the target population of adults with complex service needs. Implementation strengths included the broad recognition of existing service gaps, the use of interdisciplinary teams and experienced service providers, broad partnerships and collaboration among various service sectors, training and team building activities. Emerging challenges included lack of complementary support services such as suitable housing, organizational contexts reluctant to embrace change and risk associated with complexity, as well as limited service provider and organizational capacity to deliver evidence-based interventions. CONCLUSIONS: Findings identified implementation drivers at the practitioner, program, and system levels, specific to the implementation of community mental health interventions for adults with complex health and social needs. These can inform future efforts to address the health and support needs of this vulnerable population.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".