Bibliographic record
Abstract
Purpose of review This review focuses on recent international trends in training in forensic psychiatry, in the areas of subspecialty training in forensic psychiatry and specialty training in psychiatry. Recent findings The past few years have seen a growing acceptance of the subspecialty of forensic psychiatry in many countries. Increasingly, training programs and subspecialty certification in forensic psychiatry are available in the US. The UK, Canada, Israel, and some continental countries have provided increasing recognition of the subspecialty of forensic psychiatry, and, in the UK in particular, the field of forensic psychotherapy has grown up within the subspecialty of forensic psychiatry. In Australia and New Zealand, there has been the beginning of a movement to increase the importance of training in forensic psychiatry. Child and adolescent forensic psychiatry is beginning to be recognized as a mixed subspecialty in several countries. Specialty training in psychiatry in most countries reviewed now includes some exposure to didactic and clinical forensic psychiatry. Summary This review highlights the major international trends in the emergence of the subspecialty of forensic psychiatry.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".