A meta-analysis of predictors of offender treatment attrition and its relationship to recidivism.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: The failure of offenders to complete psychological treatment can pose significant concerns, including increased risk for recidivism. Although a large literature identifying predictors of offender treatment attrition has accumulated, there has yet to be a comprehensive quantitative review. METHOD: A meta-analysis of the offender treatment literature was conducted to identify predictors of offender treatment attrition and examine its relationship to recidivism. The review covered 114 studies representing 41,438 offenders. Sex offender and domestic violence programs were also examined separately given their large independent literatures. RESULTS: The overall attrition rate was 27.1% across all programs (k = 96), 27.6% from sex offender programs (k = 34), and 37.8% from domestic violence programs (k = 35). Rates increased when preprogram attrition was considered. Significant predictors included demographic characteristics (e.g., age, rw = -.10), criminal history and personality variables (e.g., prior offenses, rw = .14; antisocial personality, rw = .14), psychological concerns (e.g., intelligence, rw = -.14), risk assessment measures (e.g., Statistical Information on Recidivism scale, rw =.18), and treatment-related attitudes and behaviors (e.g., motivation, rw = -.13). Results indicated that treatment noncompleters were higher risk offenders and attrition from all programs significantly predicted several recidivism outcomes ranging from rw = .08 to .23. CONCLUSIONS: The clients who stand to benefit the most from treatment (i.e., high-risk, high-needs) are the least likely to complete it. Offender treatment attrition can be managed and clients can be retained through an awareness of, and attention to, key predictors of attrition and adherence to responsivity considerations.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it