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Record W2182959296

Offender Gender, Mental illness and Trauma Experience in Relation to Re- Contact with the Criminal Justice System

2013· dissertation· en· W2182959296 on OpenAlexaboutno aff
Kindra Joan Houle

Bibliographic record

VenueUWSpace (University of Waterloo) · 2013
Typedissertation
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsRelation (database)Criminal justiceCriminologyPsychologyMental illnessEconomic JusticePsychiatryPolitical scienceMental healthLawComputer science
DOInot available

Abstract

fetched live from OpenAlex

Female offenders’ experiences within the criminal justice system and the way in which they become involved with the criminal justice system are very different than that of male offenders. Previous research that has been conducted on female offending does show that womens’ contact with the criminal justice system can often be related to histories of abuse and to mental illness, and that these can also be related to subsequent re-contacts with the criminal justice system. \nAbuse, mental illness and gender, along with control variables (age, aboriginal identity, LSI-OR score), were investigated in a sample of 522 male and female Ontario Provincial offenders. When males and females were compared at the bivariate level using a chi-square comparison, females were found to be significantly more likely to re-contact. Abuse and mental illness were not found on their own to be significantly related to re-contact, but when the relationship between the three variables was examined, mental illness was found to be both significant and positively correlated to both gender and abuse. Examination into the relationship between the variables found a strong relationship between gender and abuse, gender and mental illness, mental illness and abuse as well as strong relationship in the three way interaction between gender, mental illness and abuse. The cross tabulation demonstrated that women who had experienced abuse were identified as being much more likely to be suffering from a mental illness. \nLogistic regression was used to model the relationship between re-contact, gender, abuse and the risk for re-contact. All possible interactions (as noted above) were included in the model, but the model that best fit the data included only the controls (age, aboriginal identity, LSI-OR score), gender, abuse, mental illness and the interaction between mental illness and gender. Results indicated that there was a significantly higherrisk for re-contact for females with mental illness, compared with men with mental illness or or to men and women without mental illness.. Even though abuse as a single variable or as part of an interaction was not found to be significantly related to re-contact, it is still of importance to note that the chi-square comparisons demonstrated that abuse is significantly related to gender and mental illness, therefore the relationship was still important when looking at the implications of the research. \nIt is recommended that future research further investigate the different needs of male and female offenders and the role that experienced physical, sexual and emotional abuse, mental illness and gender plays in not only offending behaviour, but in the treatment and rehabilitation of offenders within the provincial correctional system.

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)0.003
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 score0.069
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.029
GPT teacher head0.254
Teacher spread0.225 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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