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Record W4294073882 · doi:10.1111/nin.12521

A critical ethnographic perspective on risk and dangerousness in forensic psychiatry

2022· article· en· W4294073882 on OpenAlexafffundabout
Jean‐Laurent Domingue, Jean Daniel Jacob, Amélie Perron, Pierre Pariseau‐Legault, Thomas Foth

Bibliographic record

VenueNursing Inquiry · 2022
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversité du Québec en OutaouaisUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEthnographyCriminologyPsychologyPerspective (graphical)Vulnerability (computing)LegitimacyContext (archaeology)Interpersonal communicationMental healthSociologySocial psychologyPsychiatryPoliticsLawPolitical science

Abstract

fetched live from OpenAlex

In the Canadian forensic psychiatric context, the concepts of risk and dangerousness interact, intersect, and morph into the notion of significant threat to the safety of the public. Stemming from the results of a critical ethnography of the Ontario Review Board, this article unpacks the central role of forensic psychiatric nursing, as an example of a 'psych' discipline (e.g., psychiatry and psychology), in a system that is built to produce risky persons and to legitimize their detention and supervision. By using excerpt of interviews conducted with nurses, ethnographic observations of Review Board hearings, and other documentary artifacts, the findings illustrate how rationalizations of risk and dangerousness are contingent on space, time, and observer. Depending on the time of the assessment or on the health-care professional who performs it, different elements including, but not limited to, mental illness, interpersonal relationships, financial instability, and sexual vulnerability, are relied upon in very fluid, interchangeable, and discretionary ways to justify findings of dangerousness. Such a dynamic expands the reach of psychiatry's legitimacy at identifying risky conduct and controlling risky persons to domains very loosely associated with the notion of dangerousness. The work of Foucault and Castel provides the theoretical backdrop on which rests the discussion and the implications for nursing.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.379
Teacher spread0.337 · 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.

Study designTheoretical or conceptual
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

Citations6
Published2022
Admission routes3
Has abstractyes

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