Ethics, Empathy, and Detached Concern in Forensic Psychiatry.
Bibliographic record
Abstract
Clinical medical ethics are ruled by the principles of beneficence and non-maleficence. In forensic psychiatry, however, the duty to serve as an agent of the justice system overrules these principles; thus, examination subjects may indeed experience harms incurred by the psychiatrist's testimony. Alan Stone argued more than 30 years ago that the participation of psychiatrists in legal proceedings runs two essential and opposing risks: skewing justice to serve patients and deceiving patients to serve justice. In this article, we review the major lines of response and critique stemming from Stone's article. We focus on the use of empathy in examination and evaluation, a topic central to the ongoing discussion and debate. We then describe detached concern, a concept with a long history in medical education but new to discussions of ethics and empathy in forensic psychiatry. We conclude by proposing this concept as a useful addition to thought, discussion, and, above all, practice. We argue specifically that detached concern can help practitioners, seasoned and novice alike, to avail the benefits and manage the ethics risks of using empathy in evaluations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".