How to fail in the implementation of a risk assessment scheme or any other new procedure in your organization.
Bibliographic record
Abstract
F orty years ago in this journal, Jay Haley wrote an article entitled ‘‘How to Fail as a Psychotherapist.’’ In his article, he outlined the ‘‘daily dozen’’ of what could be construed as sub-optimal psychotherapy practices (e.g., ‘‘Insist that the problem which brought the patient into therapy is not important’’; ‘‘Insist that only years of therapy will really change a patient’’; ‘‘Avoid the poor because they will insist upon results and cannot be distracted with insightful conversations’’; ‘‘Avoid evaluating the results of therapy’’). The true purpose of Haley’s article was, of course, to show that much knowledge exists about strategies for effective, ethical psychotherapy. More than a decade ago, Christopher Webster emulated Haley in a book chapter describing how to fail as an assessor of risk of violence (‘‘The Art of Being a Failure as an Assessor: Twenty Suggestions’’). Analogously, the real purpose was to create a framework for the conduct of assessments of risk of violence that would meet or exceed general professional standards. For example, Webster satirically exhorted readers to avoid clarifying the purpose of the evaluation, using a systematic approach to assessment, and obtaining outcome data. At the time when Webster’s chapter appeared, researchers were beginning to explore the predictability of institutional and community violence. Scholars intended, in part, to test earlier views that clinicians have very limited capacity for fulfillment of this task. The publication of a variety of risk assessment schemes helped subsequent researchers substantially. Although not originally intended as predictive devices, some of these instruments (e.g., the Hare Psychopathy Checklist Revised [PCL-R]) appeared to have potential as tools for forecasting violence. Although none of these many schemes have yielded truly impressive predictive power, most have performed better than would have been expected 30 or 40 years ago. Psychometric differences among contemporary instruments tend to be small—unsurprisingly, given that item content tends to overlap considerably. As Randy Otto and Kevin Douglas have shown, the new challenge is not to find instruments with acceptable predictive power but instead to ensure fidelity of application. Design of a risk assessment device may be easier than ensuring its true-topurpose application in forensic, civil mental health, and correctional settings. Because such instruments have a proven, albeit imperfect ability to separate the patients who present risks from those whom politicians can safely ignore, there will always be a market for a good implementation
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".