A qualitative study of forensic patients' perceptions of quasi‐coercive offers of biological treatment
Bibliographic record
Abstract
At various points in the trajectory through the criminal justice system, a person may be encouraged by the hope of legal benefit to consent to medical treatment. This benefit may consist of diversion from prosecution, a favorable sentence, or parole. This form of legal encouragement has been referred to as legal leverage, quasi-coercive, or quasi-compulsory treatment. In this article, we analyze interviews with 15 men convicted of sexual offenses to explore their reactions to two hypothetical scenarios involving men consenting to a range of treatments intended to reduce risk of recidivism. In particular, we explore their reactions to quasi-coercive treatment using both real and hypothetical forms of biological therapy (e.g., drugs, brain stimulation, surgery), as opposed to psychological counselling. Here, we consider the extent to which these individuals perceive the situation to be coercive, the factors affecting these perceptions, and the ways in which physicians may mitigate perceived coercion. We found there is usually some degree of coercion identified when treatment consent is given in exchange for potential legal benefit, although this fact alone did not necessarily render the practice unacceptable. The degree of concern expressed over this potential coercion was related to the invasiveness and/or permanence of the treatment, and all participants highlighted the necessity of obtaining fully informed consent in the context of legally motivated treatment offers.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".