Evaluating the impact of motivational interviewing on engagement and outcomes in a web-based self-help intervention for gambling disorder: A randomised controlled trial
Bibliographic record
Abstract
Background: Self-paced internet interventions for gambling problems offer cost-effective, accessible, and private alternatives to traditional psychotherapy for a population that rarely seeks help. However, these interventions have been relatively slow to develop, evaluate, and deploy at scale relative to those for other addictive behaviors. Moreover, user engagement remains low despite the high interest. Motivational interviews have improved the effectiveness gambling bibliotherapy but have not been augmented with an analogous web-based self-guided program. Objectives: This trial aimed to replicate and extend prior work by translating a paperback workbook to the internet and pairing it with a single motivational interview. It was hypothesized that the motivational interview would enhance program engagement and gambling outcomes. Methods: = 155). The program was based on principles of cognitive-behavioral therapy and motivational interviewing. Gambling severity, expenditures, frequency, and duration were assessed via online questionnaires at baseline and 3-, 6-, and 12-months post-baseline, along with secondary outcomes (i.e., depression, anxiety, nonspecific psychological distress, alcohol consumption). Results: Baseline characteristics were indicative of severe gambling problems and concurrent mental health problems but not problematic alcohol consumption in this sample. Both treatment groups demonstrated roughly equal improvements across all gambling outcomes and most secondary outcomes over time, except alcohol consumption, which did not meaningfully change. Changes were most prominent by 3 months, followed by more gradual change by 6 and 12 months. Only 57 % of gamblers who were assigned to receive a motivational interview completed that interview. About 40 % of users did not complete any program modules and 11 % completed all four. No group differences in program engagement were observed, although the number of modules completed was associated with greater reductions in gambling behaviors in both groups. Discussion: The problem of user engagement with web-based self-help programs remains. There is a dose-response relationship between engagement and outcomes when engagement is measured in terms of therapeutic content completed. Conclusions: The addition of a motivational interview to a web-based self-help program for gambling problems was unsuccessful in improving engagement or outcomes. Future work should aim to make self-guided programs more engaging rather than solely making users more engaged. Trial registration: Registered on 7 July 2020 (ISRCTN13009468).
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 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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".