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Record W3216557649 · doi:10.1111/nin.12480

Abjection and the weaponization of bodily excretions in forensic psychiatry settings: A poststructural reflection

2021· review· en· W3216557649 on OpenAlexaff
Jim A. Johansson, Dave Holmes

Bibliographic record

VenueNursing Inquiry · 2021
Typereview
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsReflection (computer programming)Forensic psychiatryPsychologyForensic sciencePsychiatryPsychoanalysisPsychotherapistMedicineComputer science

Abstract

fetched live from OpenAlex

Nurses working in forensic psychiatric settings face unique challenges in practice, where they take on a dual role of custody and caring. Patient resistance is widespread within these restrictive settings and can take many forms. Perhaps the most disturbing form of resistance entails a patient's weaponization of their bodily fluids, with nurses as their target. The tendency in assigning motive for this act is to relegate to the psychopathology of the patient. This paper will adopt a poststructuralist perspective to reexamine this phenomenon as an act of resistance through the lens of Kristeva's concept of abjection. Patients confined in these settings have little sense of control, and in resistance may resort to the only thing available: their bodily fluids. By weaponizing the abject, patients actively violate and permeate the physical and psychological boundaries of nurses-the very boundaries considered crucial to safe and professional forensic psychiatric nursing practice. By recognizing this phenomenon as an act of resistance to confinement and loss of control, nurses may reorient their approach to care in forensic psychiatric settings.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.885
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.074
GPT teacher head0.375
Teacher spread0.301 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2021
Admission routes1
Has abstractyes

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