Revisiting the effectiveness of cognitive‐behavioural therapy for reducing reoffending in the criminal justice system: A systematic review
Bibliographic record
Abstract
This is the protocol for a Campbell systematic review. The objectives are as follows. The proposed systematic review is an update to, and extension of, Lipsey et al. (2007). As such we build on their previous aims to: (i) Assess and synthesise the overall impact of cognitive behavioural therapy (CBT) on offender recidivism; (ii) Examine possible sources of variability in the effectiveness of CBT. Data permitting, we will examine if the effectiveness of CBT varies by: (a) Characteristics of the CBT intervention (e.g., cognitive restructuring vs. cognitive skills training, group v. individual implementation; and/or custodial v. community setting, and/or), (b) Characteristics of the population (e.g., juveniles vs. adult offenders), (c) Implementation factors (e.g., implementing practitioner, use of structured/manualised approaches, delivery mode, and/or programme duration or intensity), (d) Evaluation methods (e.g., randomised vs. non-randomised research designs); (iv) Determine whether there is a decline in the effect of CBT on recidivism over time; and (v) Investigate whether there is an interaction between implementation factors and time in terms of the effect on recidivism.
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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 | 0.056 | 0.167 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.025 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".