“Talk with me”: perspectives on services for men with problem gambling and housing instability
Bibliographic record
Abstract
Abstract Background Problem gambling and homelessness are recognized as important public health concerns that significantly impact individuals, their friends and families, communities and broader society. We aimed to explore the experiences with health and social services of men who had histories of problem gambling and housing instability in Toronto, Ontario. Methods We used a community-based participatory approach with a multi-service agency serving low-income individuals. We conducted qualitative interviews with men (n = 30) who had experienced problem gambling and housing instability. Our interviews employed open-ended questions to elicit men’s perceptions of services related to housing instability, problem gambling and other comorbid conditions (e.g., mental illness, substance use). We reviewed relevant themes related to experiences with services (e.g., Use of and feedback on: health and social services, housing services, justice/legal aid services, substance use services, gambling services; stigma; goals; triggers; physical health; coping strategies; finances; relationships; barriers to services and recommendations for services). Results The concept of person-centred engagement was identified as a main overarching theme, and seemed to be lacking in most of the men’s experiences of services. Person-centred engagement for these men entailed empowerment and autonomy; empathy, compassion and sincerity; respectful communication; and tailored and holistic life plans. While there was a strong emphasis placed on independence, the men identified the importance of positive therapeutic relationships as being critical aspects of the recovery process. Based on our analyses, several recommendations were identified: 1) Increasing general awareness of services for problem gambling; 2) Delivering integrated services in a one-stop-shop; 3) Addressing mental health with psychotherapy and pharmacotherapy; 4) Providing timely access to prevention and recovery services; and 5) Enhancing life skills with peer support. Conclusions Our study highlighted that most of the men we interviewed were not having their health and social needs met. Services need to address the intersection of problem gambling, housing instability, and other comorbidities. Ensuring services are grounded in person-centred engagement appears to be critical for optimal service delivery.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.016 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".