Using Latent Class Analysis to Identify Need Typologies and Recidivism Likelihood Among Women Who Perpetrate Violence
Bibliographic record
Abstract
Understanding the heterogeneity of women who engage in violence is critical to provide effective treatment and reduce the likelihood of recidivism. Existing typologies of women who engage in violence have been created using mixed methodological approaches; the field would benefit from replication using a quantitative clustering method-latent class analysis (LCA)-as it is arguably more objective than methods used to date. A LCA was conducted using archival data involving 3,773 justice-impacted women in Western Canada to identify unique subgroups of women who perpetrate violence. Three distinct profiles emerged: (a) intimate partner violence (IPV)-only (40.5%), wherein almost all women reported only perpetrating domestic violence and had zero or only one previous violent conviction, (b) patterned (19.1%), wherein violence was perpetrated toward domestic partners and unknown victims, and the majority had two or more previous violent convictions, and (c) isolated (40.4%), wherein very few perpetrated domestic violence, some perpetrated violence toward unknowns, and the majority had either zero or only one previous conviction for a violent offense. Need profiles and recidivism outcomes were further analyzed as a function of group membership. As hypothesized, the group with the greatest criminal history and use of violence reported the greatest needs. Recidivism also increased as the number of dynamic needs increased. Notably, 80.9% of the sample was predominantly low risk/low need and were identified as IPV-only or isolated women. Implications of these findings may be used to inform risk classification, treatment targets, and treatment intensity required to reduce the likelihood of recidivism among women who perpetrate violence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".