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Record W3209942188 · doi:10.1111/nup.12375

Gilles Deleuze's societies of control: Implications for mental health nursing and coercive community care

2021· article· en· W3209942188 on OpenAlexaff
Étienne Paradis-Gagné, Dave Holmes

Bibliographic record

VenueNursing Philosophy · 2021
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsUniversity of OttawaUniversité de Montréal
Fundersnot available
KeywordsDisciplinePower (physics)SociologyControl (management)Mental illnessMichel foucaultNursingMental healthPsychologyMedicinePsychiatrySocial scienceLawPolitical sciencePoliticsManagement

Abstract

fetched live from OpenAlex

Since the era of deinstitutionalisation, many clinical approaches have emerged to enable the care and treatment of people suffering from mental illness. In recent years, the use of coercive approaches in the community (e.g., outpatient commitment or community treatment orders) has also increased internationally. Although nurses' role regarding these coercive approaches is central and significant, few empirical and theoretical writings have tackled this controversial nursing practice. The purpose of this paper is to analyse coercive nursing care through the lens of French philosopher Gilles Deleuze's concept of 'societies of control'. Taking up Michel Foucault's work on disciplinary power, Deleuze explores how the move from the striated spaces of closed institutions to the smooth spaces of societies of control took place since the middle of the 20th century. According to Deleuze, the overall objective of 'societies of control' is no longer simply to govern deviant behaviour in closed environments (e.g., psychiatric hospitals and prisons) but to ensure a regime of unrelentless surveillance in the open spaces of our communities.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.062
Scholarly communication0.0070.006
Open science0.0010.004
Research integrity0.0050.005
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 head0.088
GPT teacher head0.442
Teacher spread0.354 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2021
Admission routes1
Has abstractyes

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