Protocol for a randomized controlled trial of the Breaking Free Online Health and Justice program for substance misuse in prison settings
Bibliographic record
Abstract
BACKGROUND: Substance misuse, including problematic drug and alcohol use, are significant issues in society that can have multiple detrimental effects. Many people access support for their substance misuse during prison sentences, due to the associations between substance misuse and offending, and the high proportion of the prison population who have drug and alcohol issues. Breaking Free Online Health and Justice is a computer-assisted therapy program that has been developed to support substance-involved offenders to address their substance misuse and associated offending within prison settings. METHODS: This will be a parallel-group randomized controlled trial of 4-week Breaking Free Online Health and Justice program as an adjunct to standard treatment for substance misuse, in comparison to standard treatment only, in a male Category D open prison. Interventional and control groups will be compared in terms of the changes in their scores on multiple measures from baseline to post-treatment assessment at 4-weeks, and then 3- and 6-months follow-up. Participants will be adult male offenders serving sentences in prison in England who have demonstrable difficulties with drugs and/or alcohol for at least the past 12-months. The primary outcome measure will be self-reported substance misuse, with secondary outcomes being standardized psychometric assessments of substance dependence, mental health, biopsychosocial functioning, quality of life and post-release offending. Other secondary measures will include frequency of completion of specific intervention strategies in the program. DISCUSSION: This study will examine whether Breaking Free Online Health and Justice as an adjunct to standard substance misuse interventions in prisons, improves outcomes for substance-involved offenders receiving interventions in custodial settings. Findings from the study will be used to inform further developments of the program and potential improvements to custodial treatment. TRIALS REGISTRATION: ISRCTN09846981 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".