FC20-01 - Cultural competent assertive community treatment team for severe and persistently mentally ill: Innovations and adaptations
Bibliographic record
Abstract
The Cultural Competent Assertive Community Treatment Team (ACTT) is a joint venture sponsored by Mount Sinai Hospital in partnership with Hong Fook Mental Health Association established in 1999. This is the first ever cultural diverse mental health program using the cultural competency model. It was developed in response to the cultural diverse needs from the culturally diverse communities and supported with the best evidence-based research. Since the implementation of the program, we focused on identifying key issues that have facilitated or hindered the therapeutic alliances, patients’ service utilization and treatment compliance because of cultural differences in health concepts and health care systems. We will present what we have learned in literature review and the best evidence-based practice guidelines. Key cultural issues we have encountered with our clients will be described. Methods and strategies that are effective in overcoming cultural differences will be highlighted. We will use a case illustration to demonstrate our innovations and adaptations and will highlight lessons and challenges we have learned during the process of developing the cultural competency model. Feedbacks from the audience would be also incorporated to formulate the “best practice” guidelines. The guidelines will contribute significantly to the understanding of the complexity of cultural factors in treating mental illness but also ensure the effective service delivery to cultural diverse population. Cultural Competent Assertive Community Treatment Team has won American Psychiatric Foundation Advancing Minority Mental Health Award as well as the Leading Practice Award from Ontario Hospital Association in 2007.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".