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Record W1975279068 · doi:10.3109/13668250.2012.685149

Relationship between prior legal involvement and current crisis for adults with intellectual disability

2012· article· en· W1975279068 on OpenAlexaff
Yona Lunsky, Poonam Raina, Jessica Jones

Bibliographic record

VenueJournal of Intellectual & Developmental Disability · 2012
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsQueen's UniversityCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsIntellectual disabilityAggressionPsychologyPopulationPsychiatryMedicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Individuals with intellectual disability (ID) and legal involvement are a unique population with complex needs. To date, there has been limited research exploring the demographic and clinical profiles of individuals with ID and legal involvement that are in crisis and how they differ to individuals with ID without legal involvement. METHOD: 130 adults with ID and a history of legal involvement were compared to 617 without legal involvement who had experienced at least one crisis in terms of crisis presentation and outcome. RESULTS: Overall, those with a known history of legal involvement were younger, higher functioning and more likely to be male and living in unsupported settings. Legal history was not a significant predictor of crisis involving aggression but was a significant predictor of police response to crisis, when other variables were controlled for. CONCLUSIONS: Adults with ID and legal history may be more likely to have police respond to their crises than other individuals. Understanding the unique profiles of those with legal history can inform the development of services targeted towards offenders with ID.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.008
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.067
GPT teacher head0.345
Teacher spread0.278 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

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