Approach Versus Avoidance Goals in Relapse Prevention With Sexual Offenders
Bibliographic record
Abstract
Relapse prevention (RP) plays a major role in the vast number of treatment programs for sexual offenders. However, despite its widespread application, questions have been raised regarding the uncritical adoption of the approach (R. K. Hanson, 1996). More specifically, the way in which it is presented to sexual offenders has been criticized for being unhelpfully negative in focus (Mann, 2000) and it has been suggested that treatment should adopt a broader focus on improving quality of life (T. Ward and C. A. Stewart, 2003), with the expectation that recidivism would reduce as a side-effect. The current study evaluated two orientations to relapse prevention, comparing a deliberately positively-focused orientation, focusing on creating a "good life," to the more traditional approach that focuses on avoiding and controlling risk factors. Twenty-four participants completed an approach-focused RP intervention and 23 completed an avoidance-focused RP intervention. Results indicated that participants who completed the approach-focused intervention had a greater engagement in treatment as measured by homework compliance and willingness to disclose lapses. Furthermore, participants within the approach-focused intervention were rated by therapist to be more genuinely motivated to live life without offending by the end of treatment. However, differences between the groups in terms of self-esteem change following treatment were not very marked. These results are discussed in terms of their implications for treatment delivery.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".