When ‘help’ might hurt: Do gambling harm prevention advertisements reduce or contribute to gambling stigma? Results of an exploratory study
Bibliographic record
Abstract
OBJECTIVES: Gambling stigma is an important issue which influences, for example, the low rates of help-seeking observed internationally. Harm prevention advertisements aim to increase awareness regarding gambling harms among the population, although little empirical evaluation has been performed to see if current campaigns reduce or might alternatively contribute to gambling stigma. We therefore designed an exploratory mixed methods study to explore the effects of five UK-based harm prevention advertisements. STUDY DESIGN: Online experiment. METHODS: Participants (N = 498) completed two blocks containing the Problem Gambling Severity Index (PGSI), and the five advertisements shown in random order. Three Likert items were summed to measure each advertisement for stigmatization (e.g., "This advertisement makes me think that people who gamble heavily are at fault for whatever may happen to them"), and participants also wrote perspectives via a text box. The Likert items were analyzed via mixed models, and the text perspectives subjected to a thematic analysis. RESULTS: Quantitative results showed that one advertisement (called "chasing losses" here) was associated with a mean stigmatization score indicating agreement that it could contribute to gambling stigma. This was not the case for the remaining advertisements. Furthermore, participants with higher PGSI scores tended to give higher stigmatization scores for all advertisements. The thematic analysis supported these findings, with participants also suggesting how the adverts could be improved. CONCLUSIONS: The designers of harm prevention advertisements should consider their potential contribution to stigma. The higher stigmatization scores from those with higher PGSI scores underscores the need for multiple gambling-related harm interventions to help people experiencing gambling harms.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".