MétaCan
Menu
Back to cohort
Record W4200141943 · doi:10.1177/00048674211067164

Homicide associated with psychotic illness: What global temporal trends tell us about the association between mental illness and violence

2021· article· en· W4200141943 on OpenAlexaff
Alexander I. F. Simpson, Stephanie R. Penney, Roland M. Jones

Bibliographic record

VenueAustralian & New Zealand Journal of Psychiatry · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsHomicideMental illnessPsychiatryPoison controlInjury preventionPopulationSuicide preventionMedicineHuman factors and ergonomicsOccupational safety and healthPsychologyDemographyMedical emergencyMental healthEnvironmental health

Abstract

fetched live from OpenAlex

Available evidence suggests that persons with serious forms of mental illness are 4-10 times more likely to commit homicide as compared to non-affected members of the general population. The relationship between homicide and psychotic illness has now been subject to longitudinal investigation in six different populations across eight studies covering time periods over the last six decades. With the exception of one study, these investigations demonstrate that homicide associated with psychotic illness appears relatively stable through time and, in most populations, is not related to factors that contribute to the rise and fall of total population homicide (TPH) rates. This suggests that illness and treatment factors are of most importance if we are to reduce the prevalence of this tragic illness complication.

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)0.002
metaresearch head score (Gemma)0.008
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.018
GPT teacher head0.299
Teacher spread0.280 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAustralian & New Zealand Journal of PsychiatrySame topicPsychopathy, Forensic Psychiatry, Sexual OffendingFrench-language works237,207