Bibliographic record
Abstract
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.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".