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Record W4283703780 · doi:10.29158/jaapl.210102-21

Characteristics of Older Defendants Referred for Forensic Evaluations.

2022· article· en· W4283703780 on OpenAlexaboutno aff
Susan Hatters Friedman, Boaz Competente, Jeremy Skipworth, R D Worrall

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsnot available
Fundersnot available
KeywordsForensic sciencePsychiatryForensic examinationMedicineQuarter (Canadian coin)DementiaForensic psychiatryPopulationFamily medicinePsychologyForensic engineeringDiseaseEnvironmental health

Abstract

fetched live from OpenAlex

This study aimed to provide a descriptive analysis of the geriatric forensic population referred to a Regional Forensic Psychiatric Service by the court for evaluation (as inpatient, outpatient, or while incarcerated) in New Zealand, over a 7-year period. Data were collected retrospectively from forensic hospital records, including court-ordered reports for those aged 60 and older. Two-fifths (42%) of the 97 referred study subjects were diagnosed with some form of cognitive impairment such as dementia. The majority had a prior history of offending. Two-fifths (39%) were facing sexual charges, and one-third (33%) violent charges. Over one-quarter (28%) of the elderly sample was found unfit (incompetent) to stand trial. A better understanding of this group is needed to ensure forensic assessments and health and social services meet their various psychiatric needs.

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)0.000
metaresearch head score (Gemma)0.004
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.070
GPT teacher head0.324
Teacher spread0.253 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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Same venuePubMed→Same topicElder Abuse and Neglect→French-language works237,207→