Filling the GAP: Integrating a gambling addiction program into a shelter setting for people experiencing poverty and homelessness
Bibliographic record
Abstract
The burden of harm from problem gambling weighs heavily on those experiencing poverty and homelessness, yet most problem gambling prevention and treatment services are not designed to address the complex needs and challenges of this population. To redress this service gap, a multi-service agency within a shelter setting in a large urban centre developed and implemented a population-tailored, person-centred, evidence-informed gambling addiction program for its clients. The purpose of this article is to report on qualitative findings from an early evaluation of the program, the first designed to address problem gambling for people experiencing poverty and/or homelessness and delivered within a shelter service agency. Three themes emerged which were related to three program outcome categories. These included increasing awareness of gambling harms and reducing gambling behaviour; reorienting relationships with money; and, seeking, securing, and stabilizing shelter. The data suggest that problem gambling treatment within the context of poverty and homelessness benefits from an approach and setting that meets the unique needs of this community. The introduction of gambling treatment into this multi-service delivery model addressed the complex needs of the service users through integrated and person-centered approaches to care that responded to client needs, fostered therapeutic relationships, reduced experiences of discrimination and stigma, and enhanced recovery. In developing the Gambling Addiction Program, the agency drew on evidence-based approaches to problem gambling treatment and extensive experience working with the target population. Within a short timeframe, the program supported participants in the process of recovery, enhancing their understanding and control of their gambling selves, behaviours, and harms. This project demonstrates that gambling within the context of poverty requires a unique treatment space and approach.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".