MétaCan
Menu
Back to cohort
Record W6945139156 · doi:10.25384/sage.c.4840092.v1

A Typology of Lifetime Criminal Justice Involvement Among Homeless Individuals With Mental Illness: Identifying Needs to Better Target Intervention

2020· other· en· W6945139156 on OpenAlexaboutno aff

Bibliographic record

VenueSage Journals Data · 2020
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsTypologyCriminal justicePsychosocialMental illnessIntervention (counseling)Mental healthPsychological interventionLatent class model

Abstract

fetched live from OpenAlex

This study aimed to characterize lifetime criminal involvement among homeless people with mental illness in Canada (<i>N</i> = 1,682). A latent profile analysis yielded five classes. Most participants fell within the Fewer Needs (75.5%) group, characterized by less complex psychosocial histories and few criminal charges. Participants with Extensive Criminogenic Needs (5.0%) and Acute and Extensive Criminogenic Needs (5.0%) had more charges for justice administration, violent, and mischief/public order offenses and were more likely to have been charged before their first homelessness episode. Participants with Needs Associated with Homelessness (10.6%) and Needs Associated with Drugs (3.8%) were similar, although the former had the longest history of homelessness and the latter had more drug-related charges and were most likely to have drug use disorder. This typology, which sheds light on the cumulative needs associated with different patterns of lifetime criminal involvement among homeless people with mental illness, could guide prevention initiatives and intervention strategies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.350
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0250.001

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 head0.058
GPT teacher head0.334
Teacher spread0.276 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueSage Journals DataFrench-language works237,207