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Record W4405947157 · doi:10.1177/00938548241307233

Criminogenic and Noncriminogenic Needs in Men and Women Who Self-Report Intimate Partner Violence

2024· article· en· W4405947157 on OpenAlexaff
Dana L. Radatz, N. Zoe Hilton

Bibliographic record

VenueCriminal Justice and Behavior · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsWaypoint Centre for Mental Health CareUniversity of Toronto
FundersNational Institute of Justice
KeywordsDomestic violencePoison controlHuman factors and ergonomicsSuicide preventionPsychologyOccupational safety and healthInjury preventionIntimate partnerMedical emergencySocial psychologyClinical psychologyMedicine

Abstract

fetched live from OpenAlex

Little is known regarding the criminogenic needs of men and women who self-report intimate partner violence (IPV). We examined criminogenic and noncriminogenic needs of men and women within the general population who self-reported in three groups: perpetrating physical IPV, perpetrating non-IPV physical violence, and nonviolence perpetration. The sample included 847 men and 1437 women from the Interpersonal Conflict and Resolution (iCOR) Study. Those who self-reported IPV exhibited criminogenic and noncriminogenic needs, ranging from antisocial personality patterns (10%) to criminal associates (69%). Participants who self-reported engaging in IPV had the most criminogenic and noncriminogenic needs, had similar needs to those reporting non-IPV violence, and had consistently more needs than the nonviolence group. Overall, women reported more noncriminogenic needs than men. Community-based IPV treatment programs accepting individuals from multiple referral sources should anticipate variation in criminogenic and noncriminogenic needs among participants, especially relative to gender and referral type.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.344
Teacher spread0.310 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations4
Published2024
Admission routes1
Has abstractyes

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