A Multi-Wave Longitudinal Examination of how Strengths and Risks Inform Risk Assessment and Treatment Profiles For Justice-Involved Men and Women using The Service Planning Instrument (SPIn)
Bibliographic record
Abstract
Research is needed focusing on the predictive nature of dynamic risk and strength score changes and whether there are gender differences. Study 1 used a sample of 11,953 men and 2,877 women under community supervision with Service Planning Instrument re-assessment data. Using a multi-wave longitudinal design, hierarchical linear modeling was conducted to assess patterns of change in total dynamic risk and strength scores over a 30-month period. Change parameters (intercepts and slopes) were incorporated into regression models for each gender linking change to technical violations, new charges, violent charges, and any negative outcome. Total dynamic risk scores decreased overtime and total dynamic strength scores increased overtime for both genders. For men, change in dynamic risk scores was predictive of all four outcomes, whereas for women, change in dynamic risk scores was not predictive of violent charges. Change in dynamic strength scores predicted technical violations for both genders. Using 6,675 men and 1,684 women, Study 2 examined typologies of gender-informed risks and strengths. Latent profile analyses were conducted for men and women at three timepoints: Time 1 = first assessment, Time 2 = 3 to 8 months post initial assessment, and Time 3 = 9 to 14 months post initial assessment. Three profiles consistently emerged at each timepoint for women: low risk/low strength; gender-responsive, low risk/high strength; and aggressive, complex need/low strength (scoring high on both gender-responsive needs and gender-neutral risk factors). Men were classified into five profiles: low risk/low strength; aggressive, complex need/low strength; moderate risk/moderate strength; low risk/high strength; and low stability, complex need/low strength. At Time 3, a sixth profile of men emerged: moderate complex need/low strength. Profiles characterized as aggressive with complex needs had the highest rate of reoffending for both genders. Findings indicate that both men and women exhibit minimal changes in total dynamic risk and strength scores overtime. While change scores on total dynamic risk are predictive of reoffending outcomes, more research assessing the dynamic nature of strengths is needed to further inform risk assessment protocols. Results indicate more similarities than differences in typological structure of men and women, although heterogeneity for men increased overtime.
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 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".