“All you’ve got to do is stop”: A Qualitative Examination of Gambling Stigma and Discrimination from the Perspective of Lived Experience
Bibliographic record
Abstract
People with lived experience have drawn attention to gambling stigma as a harm in itself, justifying discrimination and exacerbating other harms. The gambling establishment’s response has reproduced individual responsibility by reducing stigma to a barrier to help-seeking. More recently, adapting to critiques of individual responsibility, the gambling establishment has expanded the issue to one of services and society. This paper identifies the structural dynamics that drive gambling stigma and discrimination from the perspective of lived experience. Semi-structured interviews were conducted with adults in Great Britain who had experienced gambling harm (n = 40). Several key themes were identified: (1) Harmless fun and individual responsibility; (2) Comparison with substance use; (3) The role of money; (4) Lack of parity in government policy; (5) Stereotypes of “typical” gamblers. The findings show the fundamental driver of stigma is the way commercial gambling functions and is enabled to function by the state, thus perpetuating the very conditions producing stigma in the first place. Stigma-reduction strategies that focus on changing individual behaviour or public information campaigns that tell people to get help early are insufficient: they are just another version of “responsible gambling,” where the individual is expected to do everything. Change requires addressing the unique features of gambling harm, stigma and discrimination, and the position the U.K. government allows commercial gambling to occupy.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".