Qualitative Experience of Self-Exclusion Programs: A Scoping Review
Bibliographic record
Abstract
Gambling disorder is a major public health issue in many countries. It has been defined as a persistent, recurrent pattern of gambling and is associated with substantial distress or impairment, lower quality of life, and living with a plurality of psychiatric problems. Many people suffering from gambling disorder seek help in ways other than formal treatment seeking, including self-management strategies. One example of responsible gambling tools that has gained popularity in recent years is self-exclusion programs. Self-exclusion entails individuals barring themselves from a gambling venue or a virtual platform. The aim of this scoping review is to summarize the literature on this topic and to explore participants' perceptions and experiences with self-exclusion. An electronic literature search was conducted on 16th May 2022 in the following databases: Academic Search Complete, CINAHL Plus with Full Text, Education Source, ERIC, MEDLINE with Full Text, APA PsycArticles, Psychology and Behavioral Sciences Collection, APA PsychInfo, Social Work Abstracts, and SocINDEX. The search yielded a total of 236 articles, of which 109 remained after duplicates were removed. After full-text reading, six articles were included in this review. The available literature shows that although there are many barriers and limitations to the current self-exclusion programs, self-exclusion is generally viewed as an effective responsible gambling strategy. There is a clear need to improve the current programs by increasing awareness, publicity, availability, staff training, off-site venue exclusion, and technology-assisted monitoring, as well as by adopting more holistic management approaches to gambling disorders in general.
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 | 0.034 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".