Adapting the assertive community treatment (ACT) for the needs of different communities: A comparative case study of KUINA ACT Japan and Mt. Sinai ACT Canada
Bibliographic record
Abstract
In this workshop, we will present the assertive community treatment (ACT) model in both Japan and Toronto, Canada. We will compare the adaptations of ACT models in both teams in order to serve their target populations efficiently and effectively. We will also compare the demographic data, clinical data and the outcomes of both ACT teams by analysing the hospitalisation days, number of emergency admission and the number of admissions into hospitals. We will also highlight differences in the mental health systems in Japan and Canada in an attempt to formulate guidelines to ensure the effectiveness of ACT Teams in both countries. We would also like to open up discussion with the audiences and incorporate their ideas and suggestions in an attempt to formulate a competent mental health system which would effectively cater to the needs of people suffering severe mental health symptoms to ensure successful integration into the community. Learning objectives: – To explore adaptation in implementation of ACT in Japan and Canada; – to develop a framework or model for assessing issues critical in establishing ACT in different countries; – to develop guidelines to establish programs which will continuously be revised implementation based on needs, systems and feedback from the field. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.032 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| 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".