Self-Management Strategies for Problem Gambling in the Context of Poverty and Homelessness
Bibliographic record
Abstract
Problem gambling and gambling disorder are serious public health issues that disproportionately affect persons experiencing poverty, homelessness, and multimorbidity. Several barriers to service access contribute to low rates of formal treatment-seeking for problem gambling compared with treatments for other addictions. Given these challenges to treatment and care, self-management may be a viable alternative or complement to formal problem gambling interventions. In this study, we described problem gambling self management strategies among persons experiencing poverty and homelessness. We conducted semi-structured interviews with 19 adults experiencing problem gambling and poverty/homelessness, and employed qualitative content analysis to code and analyze the data thematically. We identified five types of self-management strategies: (1) seeking information on problem gambling, (2) talking about gambling problems, (3) limiting money spent on gambling, (4) avoiding gambling providers, and (5) engaging in alternative activities. Although these strategies are consistent with previous research, the social, financial,housing, and health challenges of persons experiencing poverty and homelessness shaped their self-management experiences and approaches in distinct ways. Approaches to problem gambling treatment should attend to the broader context in which persons experience and attempt to self-manage problem gambling.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".