Constructing mentally ill inmates: nurses’ discursive practices in corrections
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.020 | 0.042 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".