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Nursing so-called monsters

2009· review· en· W2141762781 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Forensic Nursing · 2009
Typereview
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of OttawaSocial Sciences and Humanities Research Council
Fundersnot available
KeywordsDisgustFeelingEmpathyForensic nursingPsychologyAngerPsychotherapistSocial psychologyNursingPoison controlMedicine

Abstract

fetched live from OpenAlex

Forensic psychiatric nurses work with individuals who may evoke feelings of empathy as well as feelings of disgust, repulsion, and fear. The main objective of this theoretical paper is to engage the readers in a theoretical reflection regarding the concepts of abjection and fear since they both apply to the experiences of caring for mentally ill individuals in forensic psychiatric settings. Our contention is with the potential impact of feelings such as disgust, repulsion, and fear on the therapeutic relationship and, more particularly, with the boundaries imposed on this relationship when these feelings are unrecognized by nurses. Acknowledging that patients may evoke feelings of disgust, repulsion, and fear is essential if nurses wish to understand the implications of these emotions in the therapeutic process. In forensic psychiatric settings, caring for so-called "monsters" in the face of abjection and fear is not an easy task to achieve given the lack of theoretical understanding regarding both concepts. Given the actual state of knowledge in forensic nursing, we argue that theoretical (conceptual) analyses, as well as ethical and political discussions, are paramount if we wish to understand the specificities of this complex field of nursing practice.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.059
GPT teacher head0.412
Teacher spread0.353 · 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