Stigma-related predictors of help-seeking for problem gambling
Bibliographic record
Abstract
Stigma has been identified as a common barrier to help-seeking for problem gambling behaviors, and it is estimated that globally, only 20% of those who experience gambling problems seek help. Despite existing knowledge that stigma can play a substantial role in peoples’ willingness to seek help, there is a paucity of gambling-related research focused on stigma. In order to improve understanding of the relationships between problem gambling, gambling-related stigma, and help-seeking, this study aimed to examine how different types of stigma and various ways of coping with stigma relate to help-seeking behavior. A sample of N = 355 people who had experienced past six-month problem gambling (n = 47 help-seekers and n = 308 non-help-seekers) completed an online survey about their gambling and help-seeking behaviors and experiences with gambling-related stigma. Results showed that help-seeking was positively predicted by experienced stigma, negatively predicted by ostracism-related perceived stigma, and negatively predicted by the use of secrecy to cope with stigma. Implications of this research include an improved understanding of the relationship between stigma and help-seeking behavior, which can inform the development of more effective treatment strategies for individuals who seek help for problem gambling.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".