Age and motivation can be specific responsivity features that moderate the relationship between risk and rehabilitation outcome.
Bibliographic record
Abstract
OBJECTIVE: Specific responsivity features are not directly targeted in offender rehabilitation programs but may impact a client's receptivity. We investigated if two features may explain why high-quality correctional programs do not uniformly impact all high-risk, high-needs clients. HYPOTHESES: The current study was exploratory. We hypothesized a relationship between higher static risk and poorer program outcomes and then explored if this relationship was attenuated by age and motivation. METHOD: Program providers rated the performance of incarcerated males (n = 2,417, Mean age = 33.6, SD = 9.9, Range = 18-81) who attended one of six types of programs during incarceration (for general, violent, and sexual offenders). Using risk scores calculated at prison entry, we predicted performance and official record recidivism. Preprogram motivation and age were moderators. RESULTS: Five of 24 exploratory multilevel models revealed an attenuated relationship between risk and program outcome among older offenders (percent variance explained = 17.9% within violence groups; 11.5% within living skills groups; and a 9% difference in predicted recidivism rates among high-risk attendees of family violence groups) and offenders with higher preprogram motivation (percent variance explained = 43.6% within violence groups, and a 7% difference in predicted recidivism rates among high risk attendees in living skills groups). CONCLUSIONS: Age and motivation can be specific responsivity features that may deserve attention in rehabilitation practice. However, observed effects may have been weakened by underdeveloped, single-indicator measurement strategies. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".