The impact of training and coaching on the development of practice skills in youth justice: findings from Australia
Bibliographic record
Abstract
Introduction A number of studies have found that the skills and practices of probation and parole officers, and others who supervise offenders in the community, have an impact on the recidivism rates of offenders under supervision. Studies in Australia, Canada, the United Kingdom and the United States have found that when probation officers use particular supervision skills, offenders under their supervision have recidivism rates as much as 60% lower than offenders supervised by workers who do not use these skills (Trotter, 2013). The impact applies to both reoffending and compliance with conditions. The argument presented in the literature is not that correctional interventions always work, but that appropriate forms of intervention can be effective. In a review of meta-analysis of treatment effectiveness, Andrews and Bonta (2006, p 329) argued that appropriate treatment led to reductions in recidivism of ‘a little more than 50 percent from that found in comparison conditions’. Effective practice skills My review of studies on the effectiveness of offender supervision (Trotter, 2013) found that the studies identified similar supervision skills as being effective. These include role clarification, prosocial modelling and reinforcement, problem solving, cognitive-behavioural techniques and relationship factors. These skills are generally more effective when used with medium- to high-risk offenders (Trotter, 2013). Role clarification Work with offenders involves what Ronald Rooney (2009) and Jones and Alcabes (1993) refer to as client socialisation, or what others have referred to as role clarification (Trotter, 2015). One aspect of role clarification involves helping the client to accept that the worker can help with the client's problems even though the worker has a social control role. Other aspects of role clarification involve exploring the client's expectations, helping the client to understand what is negotiable, the limits of confidentiality and the nature of the worker's authority. Some research has been undertaken on this issue in mental health (Videka-Sherman, 1988) and in child protection (Trotter, 2004). Less work has been done in corrections settings, although several studies (for a review, see Trotter, 2013) found that role clarification skills were part of a group of skills that related to reduced reoffending by probationers.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".