The Situational Analysis of Problem Gamblers in Recovery: An Action Research Study
Bibliographic record
Abstract
As of 2012, there were more than 100 studies on the prevalence and social impact of problem gambling in Canada. However, few qualitative psychological studies specifically explored the process of therapy for problem gamblers. This research initiative attempts to bridge this notable gap in the literature. In this action research inquiry, I explore the recalled experiences of therapeutic interactions of six problem gamblers and six registered clinical counsellors. The participants engaged in focus groups, individual interviews, and a teleconference that helped to foster a greater understanding of the stigma attached to “being” a problem gambler. Although there are many comprehensive works on the topic of stigma, I felt the quintessential work of Erving Goffman (1963) on the moral management of stigma and spoiled identity was a fitting lens through which to examine the plight of the problem gambler in counselling. In this exploratory inquiry, I found many examples of participant gamblers morally managing their identities, agentively adopting, challenging, or resisting various descriptions of their stigmatized status. My situational analyses highlighted how clients engaged in “healthy” resistance, agentively managing the potential for stigmatized or spoiled identities. These shared narratives brought awareness to the marginalized status of “being” a problem gambler, while providing an appreciation of ways in which clients and counsellors can collaboratively promote preferred ways of being. These findings also indicate that further sensitivity may be required for practitioners providing services to stigmatized populations, such as the individual with gambling problems. Implications for counselling theory, practice, and possible future research are discussed.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".