Gender differences in felt stigma and barriers to help-seeking for problem gambling
Bibliographic record
Abstract
BACKGROUND: Men and women differ in their patterns of help-seeking for health and social problems. For people experiencing problem gambling, feelings of stigma may affect if and when they reach out for help. In this study we examine men's and women's perceptions of felt stigma in relation to help-seeking for problematic gambling. METHODS: Using concept mapping, we engaged ten men and eighteen women in group activities. We asked men and women about their perceptions of the pleasurable aspects and negative consequences of gambling; they generated a list of four hundred and sixteen statements. These statements were parsed for duplication and for relevance to the study focal question and reduced to seventy-three statements by the research team. We then asked participants to rate their perceptions of how much felt stigma (negative impact on one's own or family's reputation) interfered with help-seeking for gambling. We analyzed the data using a gender lens. FINDINGS: Men and women felt that shame associated with gambling-related financial difficulties was detrimental to help-seeking. For men, the addictive qualities of and emotional responses to gambling were perceived as stigma-related barriers to help-seeking. For women, being seduced by the 'bells and whistles' of the gambling venue, their denial of their addiction, their belief in luck and that the casino can be beat, and the shame of being dishonest were perceived as barriers to help-seeking. CONCLUSIONS: Efforts to engage people who face gambling problems need to consider gendered perceptions of what is viewed as stigmatizing.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".