Predictors of Treatment Attrition as Indicators for Program Improvement not Offender Shortcomings: A Study of Sex Offender Treatment Attrition
Bibliographic record
Abstract
This study classified potential attrition predictors under the domains of risk, need and responsivity (D. Andrews & J. Bonta, 2003). Non-sexual criminogenic needs (e.g. aggression, rule violating behaviors) and responsivity factors (e.g. lack of motivation and denial) were the two main clusters of predictors that correctly classified 95.3% of program completers and non-completers using discriminant function analysis in a sample of high-risk male sexual offenders treated in an accredited inpatient sex offender treatment program. Rapists were more aggressive than other types of sex offenders and were more likely to drop out of treatment. Some studies of predictors of treatment attrition have used offender problem behaviors or psychopathologies to predict attrition and then use the information to exclude offenders from treatment. Others have argued, and we concur, that results of attrition research should not be used to develop an "attrition profile" to exclude offenders from treatment. Predictors of attrition should be seen as markers for program improvement, rather than shortcomings of the offender. Suggestions for program improvements to reduce the rate of attrition, based on results of research, are presented.
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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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".