Young people in Australia discuss strategies for preventing the normalisation of gambling and reducing gambling harm
Bibliographic record
Abstract
BACKGROUND: The normalisation of gambling for young people has received considerable recent attention in the public health literature, particularly given the proliferation of gambling marketing aligned with sport. A range of studies and reports into the health and wellbeing of young people have recommended that they should be consulted and engaged in developing public health policy and prevention strategies. There are, however, very few opportunities for young people to have a say about gambling issues, with little consideration of their voices in public health recommendations related to gambling. This study aimed to address this gap by documenting young people's perceptions about strategies that could be used to counter the normalisation of gambling and prevent gambling related harm. METHODS: This study took a critical qualitative inquiry approach, which acknowledges the role of power and social injustice in health issues. Qualitative interviews, using a constructivist approach, were conducted with 54 young people (11-17 years) in Australia. Reflexive thematic analysis was used to interpret the data. RESULTS: Five overall strategies were constructed from the data. 1) Reducing the accessibility and availability of gambling products; 2) Changing gambling infrastructure to help reduce the risks associated with gambling engagement; 3) Untangling the relationship between gambling and sport; 4) Restrictions on advertising; and 5) Counter-framing in commercial messages about gambling. CONCLUSIONS: This study demonstrates that young people have important insights and provide recommendations for addressing factors that may contribute to the normalisation of gambling, including strategies to prevent gambling related harm. Young people hold similar views to public health experts about strategies aimed at de-normalising gambling in their local communities and have strong opinions about the need for gambling to be removed from sport.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".