Peer Support Facilitated Online Cognitive Behavioural Therapy for Substance Use Disorder: Lessons Learned from a Peer Support Perspective
Bibliographic record
Abstract
Despite the high prevalence of substance use-related harms, the majority of individuals affected do not seek formal treatment. Digital interventions, such as Breaking Free Online (BFO) based in CBT, are being used to bridge this gap through improved accessibility and responding to an increasingly online world. However, many individuals who interact with digital mental health receive little or no human support. Peer support— defined by support grounded in shared lived experience and the values of hope, empathy and self-determination—may offer a valuable way to enhance engagement with digital tools. This presentation outlines the implementation of peer-facilitated BFO within a randomized controlled trial. 197 Participants were randomized to 1 of 3 arms; group peer support, BFO or individual peer support, each arm also included clinical monitoring. The study design, assessment schedule and peer support worker training was collaboratively developed with the input of an advisory committee that included lived experts and service users as well as scientists and clinicians. 66 participants—primarily diagnosed with Alcohol and Cannabis Use Disorders—were randomized to BFO with peer support. I personally provided peer support to 31 of the 66 participants. While individual preferences varied, key themes emerged. Participants consistently reported that peer support improved their ability to engage with BFO by helping them set personalized goals, challenge unhelpful thinking, stay accountable, and apply the digital content to their own lives. The biggest piece of feedback I received was “I would not have engaged with BFO as much if there wasn’t a peer support worker” Challenges that were identified, included technical barriers, missed appointments, and the need to better define and capture meaningful outcomes. Many participants described personal growth and positive changes that were not reflected in standard digital metrics. Importantly, peer supporters themselves reported benefits from the process, including professional and personal development and the opportunity to provide meaningful, person-centered care. In summary, integrating peer support with digital interventions like BFO offers a promising, scalable approach to substance use treatment. It brings structure to a complex discipline, while maintaining the flexibility and responsiveness that peer support requires. Thank you.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".