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How to fail in the implementation of a risk assessment scheme or any other new procedure in your organization.

2011· article· en· W2055969993 on OpenAlexaff
Kåre Nonstad, Christopher D. Webster

Bibliographic record

VenueAmerican Journal of Orthopsychiatry · 2011
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of TorontoSimon Fraser University
Fundersnot available
KeywordsScheme (mathematics)Risk analysis (engineering)Fail-safeRisk assessmentPsychologyComputer scienceBusinessComputer securityReliability engineeringMathematicsEngineering

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.030
GPT teacher head0.347
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2011
Admission routes1
Has abstractyes

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