Bibliographic record
Abstract
UNLABELLED: TOPIC/PROBLEM: Since 1978, the federal inmates of Canada serving time have had access to a full range of psychiatric care within the carceral system. Five psychiatric units are part of the Federal Correctional Services. Nursing practice in forensic psychiatry opens up new horizons in nursing. This complex professional nursing practice involves the coupling of two contradictory socio-professional mandates: to punish and to provide care. METHOD: The purpose of this article is to present the results of a grounded theory doctoral study realized in a multi-level security psychiatric ward of the Canadian Federal Penitentiary System. The theoretical work of the late French philosopher, Michel Foucault, and those of sociologist, Erving Goffman, are used to illuminate the qualitative data that emerged from the author's fieldwork. FINDINGS: A Foucauldian perspective allows us to understand the way forensic psychiatric nursing is involved in the governance of mentally ill criminals through a vast array of power techniques (sovereign, disciplinary, and pastoral) which posited nurses as "subjects of power". These nurses are also "objects of power" in that nursing practice is constrained by formal and informal regulations of the penitentiary context. CONCLUSION: As an object of "governmental technologies", the nursing staff becomes the body onto which a process of conforming to the customs of the correctional milieu is dictated and inscribed. The results of this qualitative research, from a nursing perspective, are the first of their kind to be reported in Canada since the creation of the Regional Psychiatric Correctional Units in 1978.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".