The ethics of forensic psychiatry: moving beyond principles to a relational ethics approach
Bibliographic record
Abstract
Forensic psychiatry has been described as a ‘moral minefield’. The competing obligations at the interface of the justice and healthcare systems raise questions about the very viability of an ethical framework for guiding practice. The explicit need for security and detention, and the implicit ‘untrustworthiness’ of forensic patients shape practitioners' everyday reality. Suspicion colors client–practitioner relationship and fundamental care concepts, such as patient advocacy, take on different nuances in this milieu. Despite the complex ethical demands of this unique practice area, it has received little attention within mainstream bioethics. There is, however, a growing imperative to find a theory of ethics for the specialty. In this article, the ethics of forensic psychiatry is examined, and it is argued that relational ethics is a fitting framework for forensic practice and, further, that forensic settings are the very place to test the validity of such an ethic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.018 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".