MétaCan
Menu
Back to cohort

Recent trends in forensic psychiatry training

2004· article· en· W2055605891 on OpenAlexaboutno aff
Joseph B. Layde

Bibliographic record

VenueCurrent Opinion in Psychiatry · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsnot available
Fundersnot available
KeywordsSubspecialtyForensic psychiatrySpecialtyPsychiatryForensic scienceCertificationMedicinePsychologyMedical educationPolitical scienceLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0180.003

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.

Opus teacher head0.089
GPT teacher head0.400
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Opinion in PsychiatrySame topicProblem and Project Based LearningFrench-language works237,207