Augmenting an online self-directed intervention for gambling disorder with a single motivational interview: study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Despite the success of gold standard cognitive-behavioral therapy for problem and disordered gambling, the majority of individuals with gambling problems do not seek or receive professional treatment. Thus, the development of less intrusive self-directed interventions has been encouraged. Bibliotherapy for problem gambling has shown promise, both alone and in combination with motivational interviews, but there is still a lack of online self-directed intervention research. The current randomized controlled trial proposes to assess the additive benefit of a single digital motivational interview delivered in conjunction with an online self-directed treatment program for problem gambling and gambling disorder. METHODS: A two-arm randomized controlled trial will be conducted, wherein eligible participants (N=270) will be recruited across Canada via internet advertisements posted to several platforms. All participants will receive access to an online self-directed gambling intervention program. Participants will be randomly assigned to either complete the online program alone or receive a digital motivational interview, conducted through an online audioconferencing platform (i.e., Microsoft Teams) to supplement the online program. The primary outcomes of gambling severity, frequency, and expenditures will be tracked along with secondary outcomes (i.e., depression, anxiety, general distress, alcohol use, and online program user data) over a 24-month period. It is expected that participants in both groups will experience a reduction in symptoms across the board, but more substantial improvements will be observed in the group that receives a supplemental motivational interview. DISCUSSION: The results of this trial will expand upon prior gambling intervention research by informing best practices for the provision of online self-help for problem gambling. TRIAL REGISTRATION: ISRCTN ISRCTN13009468 . Registered on 7 July 2020.
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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 | 0.044 | 0.037 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.101 | 0.019 |
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".