Constructing monsters: correctional discourse and nursing practice.
Bibliographic record
Abstract
This article presents the results of a nursing research that aimed at describing the practice of nursing in an extreme environment where social control and psychiatric nursing care are inextricably enmeshed with one another. The study results indicate that the Correctional Psychiatric Centre (CPC) is a site where two antagonistic discourses (that of the hospital and that of the prison) are contending for the human resources in place. Given that the asylum and the prison are as such two distinct institutions, the correctional psychiatric centre constitutes an ideological space that results from the fusion of both the psychiatric and penal apparatuses. However, characteristics commonly attributed to prisoners, such as 'lying', 'dangerous', 'monstrous' and 'manipulative' are superimposed on the nurses' common theoretical representation that a patient is a person to whom care is provided. Monstrosity was a term regularly employed to describe particular types of inmates. The literature on monsters in quite informative in order to understand the impact of such a representation of forensic psychiatric nursing practice.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.046 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".