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Constructing mentally ill inmates: nurses’ discursive practices in corrections

2011· article· en· W1521871514 on OpenAlexafffund
Amélie Perron, Dave Holmes

Bibliographic record

VenueNursing Inquiry · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsSubjectivityConstruct (python library)Discourse analysisPerspective (graphical)SociologySet (abstract data type)Power (physics)Space (punctuation)Qualitative researchNursingPsychologyEpistemologyMedicineLinguisticsSocial scienceComputer science

Abstract

fetched live from OpenAlex

The concepts of discourse, subjectivity and power allow for innovative explorations in nursing research. Discourse take many different forms and may be maintained, transmitted, even imposed, in various ways. Nursing practice makes possible many discursive spaces where discourses intersect. Using a Foucauldian perspective, were explored the ways in which forensic psychiatric nurses construct the subjectivity of mentally ill inmates. Progress notes and individual interviews constitute discursive spaces within which nurses construct patients' subjectivities. Progress notes provide a written (and permanent) form of discourse, while interviews set the space for a more fluid and contextual form of discourse. We identified five types of subjectivities - the (in)visible patient, the patient as risk, the deviant patient, the disturbed patient and the disciplined patient. These subjectivities were rooted in various types of discourses circulating in the selected setting. Despite the multiple discursive dimensions of forensic psychiatric nursing, progress notes remain the main formal source of information regarding nursing care even though it is not representative of the care provided nor is it representative of nurses' complex discursive practices in corrections.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.111
GPT teacher head0.401
Teacher spread0.290 · 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 designQualitative
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

Citations31
Published2011
Admission routes2
Has abstractyes

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