Self-management strategies among people who experience problem gambling, poverty, and/or homelessness
Bibliographic record
Abstract
Introduction/Rationale: There is a high prevalence of problem gambling among people experiencing poverty/homelessness, but several barriers contribute to low rates of formal treatment access. Assisting people with self-management strategies may be one solution to this problem. Self-management has been identified as an important occupation for people experiencing poverty/homelessness. There is growing interest in the potential of self-management strategies for problem gambling.Objectives: In this qualitative study, we aimed to describe the gambling self-management strategies among people experiencing problem gambling, poverty/homelessness, and complex health issues in a large multiethnic city in Canada.Method/Approach: Semi-structured interviews and a brief demographic survey with 19 adults experiencing problem gambling, poverty/homelessness, and complex health issues was conducted. Inductive qualitative content analysis was used to analyze participants’ gambling self-management strategies, which were then interpreted through an occupational lens.Results and or Practice Implications: Five types of gambling self-management strategies were identified: (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. These themes speak to proactive actions that directly address problem gambling issues as well as actions that result in the avoidance of things to prevent themselves from gambling. These findings can be leveraged to promote occupational self-management supports.Conclusion: Self-management was a central occupation in participants’ everyday lives, shaping their orientations to and understandings of a range situations, activities, and relationships. A combination of these strategies may be used in occupation-based interventions to effectively promote self-management skills.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".