START Changing Practice: Implementing a Risk Assessment and Management Tool in a Civil Psychiatric Setting
Bibliographic record
Abstract
The assessment and management of risk for violence among individuals with a mental illness, traditionally the responsibility of specialized forensic institutions, are now becoming increasingly necessary in civil mental health services. The Short Term Assessment of Risk and Treatability (START) is a patient-centered scheme informing multiple risk domains through a comprehensive assessment of dynamic risk factors and strengths, based on a fixed set of definitions. However, there is a dearth of literature on the implementation of such tools in real life mental health settings, their relation to future behavior and their perceived usefulness in clinical settings. In this article, we describe the process of implementing the START on a civil psychiatric hospital unit in Canada taking into account typical barriers to the dissemination of evidence-based practices. The results of a longitudinal prospective mixed method (qualitative and quantitative) implementation study indicate that the START was well integrated into the unit's clinical and administrative activities. In these times of financial constraints in mental health services, as well as the increased pressure of mental health services to manage a wide variety of challenging behaviors, risk management tools can provide help in improving patient care and empowering staff to better understand violence and other challenging behaviors. This paper concludes with a discussion of the need to develop implementation research in the field of forensic mental health services in order to reduce the gap between research and practice.
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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 | 0.001 | 0.000 |
| 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".