Exploring the Users’ Perspective of the Nationwide Self-Exclusion Service for Gambling Disorder, “Spelpaus”: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Problem gambling and gambling disorder cause severe social, psychiatric, and financial consequences, and voluntary self-exclusion is a common harm reduction tool used by individuals with gambling problems. OBJECTIVE: The aim of this study was to explore users' experience of a novel nationwide, multioperator gambling self-exclusion service, "Spelpaus," in Sweden and to inform stakeholders and policy makers in order to improve harm reduction tools against gambling problems. METHODS: Semistructured interviews were conducted with 15 individuals who reported self-perceived gambling problems and who had experience of having used the self-exclusion service Spelpaus in Sweden. Interviews were transcribed and analyzed through qualitative content analysis. RESULTS: We identified 3 categories and 8 subcategories. The categories were (1) reasons for the decision to self-exclude, (2) positive experiences, and (3) suggestions for improvement. The subcategories identified a number of reasons for self-exclusion, such as financial reasons and family reasons, and positive experiences described as a relief from gambling; in addition, important suggestions for improvement were cited, such as a more gradual return to gambling post-self-exclusion, better ways to address loopholes in the system, and transfer from self-exclusion to treatment. CONCLUSIONS: Voluntary self-exclusion from gambling, using a nationwide multioperator service, remains an appreciated harm-reducing tool. However, transfer from self-exclusion to treatment should be facilitated by policy making, and loopholes allowing for breaching of the self-exclusion need to be counteracted.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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 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".