Effectiveness of Computer-Assisted Therapy for Substance Dependence Using Breaking Free Online: Subgroup Analyses of a Heterogeneous Sample of Service Users
Bibliographic record
Abstract
BACKGROUND: Substance misuse services within the United Kingdom have traditionally been oriented to opiate and crack users, and attended predominantly by male service users. Groups who do not fit this demographic, such as women or those whose primary drug of choice is neither heroin nor crack, have tended to be underrepresented in services. In addition, there can be stigma associated with traditional opiate and crack-centric services. Therefore, the computerized treatment and recovery program, Breaking Free Online (BFO), was developed to enable service users to access confidential support for dependence on a wide range of substances. BFO is delivered as computer-assisted therapy (CAT), or, where appropriate, used as self-help. OBJECTIVE: The aim of this study was to report psychometric outcomes data from 393 service users accessing online support for substance misuse via BFO. METHODS: Following initial referral to substance misuse services, all participants were supported in setting up a BFO login by a practitioner or peer mentor, and, where required, assisted as they completed an online baseline assessment battery contained within the BFO program. Following a period of engagement with BFO, all participants completed the same battery of assessments, and changes in the scores on these assessments were examined. RESULTS: Significant improvements were found across the 393 service users in several areas of psychosocial functioning, including quality of life, severity of alcohol and drug dependence, depression, and anxiety (P=<.001 across all aspects of functioning). Additionally, significant improvements were found within specific subgroups of participants, including females (P=.001-<.001), males (P=.004-<.001), service users reporting alcohol dependence (P=.002-<.001), opiate and crack dependence (P=.014-<.001), and those seeking support for other substances that may be less well represented in the substance misuse sector (P=.001-<.001). CONCLUSIONS: Data from this study indicates that BFO is an effective clinical treatment for a wide range of individuals requiring support for substance misuse. Further work is currently underway to examine more closely the clinical effectiveness of the program.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".