Internet-Based Interventions for Problem Gambling: Scoping Review
Bibliographic record
Abstract
BACKGROUND: This study seeks to give an overview of academic research on internet-based interventions that are used to address problem gambling. The rate of treatment seeking has been demonstrated to be low across several research environments. This is in part because of the systemic barriers that treatment seekers face to accessing traditional face-to-face treatment. Making treatment resources for problem gambling available through the internet is one way to reduce the impact of those systemic barriers. The use of internet-based resources to address problem gambling has been growing, and a field of research evaluating it has developed as well. However, little has been done to summarize this collection of research. OBJECTIVE: This study aimed to provide a scoping review of the use of internet-based interventions for problem gambling treatment and prevention to provide an understanding of the current state of the field. METHODS: A scoping review was performed for 6 peer-reviewed research databases (Web of Science, PsycINFO, Cumulative Index to Nursing and Allied Health Literature, MEDLINE, Social Science Abstracts, and Scopus) and 3 gray literature databases (MedEdPortal, Proquest: Dissertations, and OpenGrey). Article inclusion criteria were as follows: published over the 10-year period of 2007 to 2017, including an intervention for problem gambling, and involving the use of internet to deliver that intervention. RESULTS: A total of 27 articles were found that met the review criteria. Studies were found from several different areas, with particularly strong representation for Australia, New Zealand, and Scandinavia. Cognitive behavioral therapy was the most common form of internet-based intervention. Internet-based interventions were generally shown to be effective in reducing problem gambling scores and gambling behaviors. A wide range of interventions that made use of internet resources included text-based interactions with counselors and peers, automated personalized and normative feedback on gambling behaviors, and interactive cognitive behavioral therapies. A lack of diversity in samples, little comparison with face-to-face interventions, and issues of changes in the treatment dynamic are identified as areas that require further investigation. CONCLUSIONS: Internet-based interventions are a promising direction for treatment and prevention of problem gambling, particularly in reducing barriers to accessing professional help. The state of the current literature is sparse, and more research is needed for directly comparing internet-based interventions and their traditional counterparts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".