The influence of marketing on the sports betting attitudes and consumption behaviours of young men: implications for harm reduction and prevention strategies
Bibliographic record
Abstract
BACKGROUND: Gambling can cause significant health and social harms for individuals, their families, and communities. While many studies have explored the individual factors that may lead to and minimise harmful gambling, there is still limited knowledge about the broader range of factors that may contribute to gambling harm. There are significant regulations to prevent the marketing of some forms of gambling but comparatively limited regulations relating to the marketing of newer forms of online gambling such as sports betting. There is a need for better information about how marketing strategies may be shaping betting attitudes and behaviours and the range of policy and regulatory responses that may help to prevent the risky or harmful consumption of these products. METHODS: We conducted qualitative, semi-structured interviews with 50 Australian men (aged 20-37 years) who gambled on sports. We explored their attitudes and opinions regarding sports betting marketing, the embedding of marketing within sports and other non-gambling community environments, and the implications this had for the normalisation of betting. RESULTS: Our findings indicate that most of the environments in which participants reported seeing or hearing betting advertisements were not in environments specifically designed for betting. Participants described that the saturation of marketing for betting products, including through sports-based commentary and sports programming, normalised betting. Participants described that the inducements offered by the industry were effective marketing strategies in getting themselves and other young men to bet on sports. Inducements were also linked with feelings of greater control over betting outcomes and stimulated some individuals to sign up with more than one betting provider. CONCLUSIONS: This research suggests that marketing plays a strong role in the normalisation of gambling in sports. This has the potential to increase the risks and subsequent harms associated with these products. Legislators must begin to consider the cultural lag between an evolving gambling landscape, which supports sophisticated marketing strategies, and effective policies and practices which aim to reduce and prevent gambling harm.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".