The use of self-management strategies for problem gambling: a scoping review
Bibliographic record
Abstract
BACKGROUND: Problem gambling (PG) is a serious public health concern that disproportionately affects people experiencing poverty, homelessness, and multimorbidity including mental health and substance use concerns. Little research has focused on self-help and self-management in gambling recovery, despite evidence that a substantial number of people do not seek formal treatment. This study explored the literature on PG self-management strategies. Self-management was defined as the capacity to manage symptoms, the intervention, health consequences and altered lifestyle that accompanies a chronic health concern. METHODS: We searched 10 databases to identity interdisciplinary articles from the social sciences, allied health professions, nursing and psychology, between 2000 and June 28, 2017. We reviewed records for eligibility and extracted data from relevant articles. Studies were included in the review if they examined PG self-management strategies used by adults (18+) in at least a subset of the sample, and in which PG was confirmed using a validated diagnostic or screening tool. RESULTS: We conducted a scoping review of studies from 2000 to 2017, identifying 31 articles that met the criteria for full text review from a search strategy that yielded 2662 potential articles. The majority of studies examined self-exclusion (39%), followed by use of workbooks (35%), and money or time limiting strategies (17%). The remaining 8% focused on cognitive, behavioural and coping strategies, stress management, and mindfulness. CONCLUSIONS: Given that a minority of people with gambling concerns seek treatment, that stigma is an enormous barrier to care, and that PG services are scarce and most do not address multimorbidity, it is important to examine the personal self-management of gambling as an alternative to formalized treatment.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".