Self-Directed Workbooks: Evaluating Their Efficacy in a U.S. Probation Setting
Bibliographic record
Abstract
Interactive Journaling ® and other structured journaling interventions have been utilized successfully in a variety of settings (e.g., mental health, addiction) and recent research has indicated that journaling with incarcerated offenders may be beneficial in reducing recidivism (e.g., Proctor, Hoffman, & Allison, 2012).The purpose of the current study was to implement and examine the effectiveness of a set of five self-directed workbooks in a community supervision context and evaluate their utility from the clients' point of view.These workbooks, based on criminogenic needs, were developed to assist parole and probation officers in increasing the community success of offenders.Each workbook targets a different factor (e.g., criminal attitudes, motivation to change) essential to managing offender behaviour.Despite organizational changes impeding implementation at one site and a low participation rate (N = 32), which led to issues of power during analyses, the preliminary results combined with previous research do suggest that selfdirected workbooks may have potential in reducing both technical violations and recidivism among supervised offenders.Implications, limitations, and directions for future research are discussed.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".