Predicting Intimate Partner Violence Perpetration Among Young Adults Experiencing Homelessness in Seven U.S. Cities Using Interpretable Machine Learning
Bibliographic record
Abstract
Young adults experiencing homelessness (YAEH) are at higher risk for intimate partner violence (IPV) victimization than their housed peers. This is often due to their increased vulnerability to abuse and victimization before and during homelessness, which can result in a cycle of violence in which YAEH also perpetrates IPV. Identifying and addressing factors contributing to IPV perpetration at an early stage can reduce the risk of IPV. Yet to date, research examining YAEH’s IPV perpetration is scarce and has largely employed conventional statistical approaches that are limited in modeling this complex phenomenon. To address these gaps, this study used an interpretable machine learning approach to answer the research question: What are the most salient predictors of IPV perpetration among a large sample of YAEH in seven U.S. cities? Participants ( N = 1,426) on average were 21 years old ( SD = 2.09) and were largely cisgender males (59%) and racially/ethnically diverse (81% were from historically excluded racial/ethnic groups; i.e., African American, Latino/a, American Indian, Asian or Pacific Islander, and mixed race/ethnicity). Over one-quarter (26%) reported IPV victimization, and 20% reported IPV perpetration while homeless. Experiencing IPV victimization while homeless was the most important factor in predicting IPV perpetration. An additional 11 predictors (e.g., faced frequent discrimination) were positively associated with IPV perpetration, whereas 8 predictors (e.g., reported higher scores of mindfulness) were negatively associated. These findings underscore the importance of developing and implementing effective interventions with YAEH that can prevent IPV, particularly those that recognize the positive association between victimization and perpetration experiences.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".