Book Review: Forensic Psychiatry: Forensic Psychiatry: Influences of Evil
Bibliographic record
Abstract
Forensic Psychiatry: Influences of Evil Tom Mason, editor. Totowa (NJ): Humana Press; 2006. 387 p. US$99.95. Reviewer rating: Not Reviewed by: J Paul Fedoroff, MD Ottawa, Ontario Ever wonder what source Tom Cruise relied upon when he said, in his now infamous interview with Matt Lauer, that he had studied the history of psychiatry? This book may be it. When I agreed to review Influences of Evil, I had assumed it would be a text about the problems caused by psychiatric patients. It is not. Instead, Influences of Evil largely refers to the nature of psychiatry itself! I am a psychiatrist, but this is why I assigned a not recommended rating. That would be evil. Rather, it is because the book begins with the premise that psychiatry is evil instead of building a case for this dramatic statement. Further, the book describes psychiatry in a way that sets it up for straw-dog arguments. Its style is reminiscent of historic antipsychiatry authors like Michel Foucault and Thomas Szasz, who relied on this technique and are frequently cited with approval in this book. There are 18 chapters, so it is possible to review each in detail. In general, psychiatry is portrayed as synonymous with psychoanalysis, though unscrupulous drug prescription is also criticized. Forensic psychiatry itself fares no better, with the resurrection of Foucault's description of the field as the pathology of the monstrous.p2 Forensic psychiatry is also described as a discipline that has grown alongside the law, and in fact has both a symbiotic and parasitic relationship with it... [and that] needs legislation to capture its clients but sits uneasily at its feet when challenged regarding therapeutic efficacy.p2-3 A few pages later, authors whom the editor calls tried and tested learned men describe psychiatry as an impotent field in which its effects are but one element in the salving of social conscience.p10 A chapter on Institutional Monstrosity explains that forensic settings are evil structures permeated by power relations.p19 Leaving aside the preceding premise, this chapter contains several peculiar comments. For example, patients and prisoners are treated synonymously by these authors and apparently any contact with a prisoner must occur in the presence of a correctional guard.p25 Similarly, it asserts that empathy and attentiveness toward inmates are perceived as feminine characteristics that are strictly forbidden ... a constraint which weighs heavily on nursing staff in general and female nurses in particular.p25 This chapter also ignores the fact that most patients live in the community and that a primary (and so far successful) aim of modern treatment is to increase this number. …
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.063 | 0.055 |
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".