Alcohol and fast food sponsorship in sporting clubs with junior teams participating in the ‘Good Sports’ program: a cross‐sectional study
Bibliographic record
Abstract
OBJECTIVE: To examine: alcohol and fast food sponsorship of junior community sporting clubs; the association between sponsorship and club characteristics; and parent and club representative attitudes toward sponsorship. METHODS: A cross-sectional telephone survey of representatives from junior community football clubs across New South Wales and Victoria, Australia, and parents/carers of junior club members. Participants were from junior teams with Level 3 accreditation in the 'Good Sports' program. RESULTS: A total of 79 club representatives and 297 parents completed the survey. Half of participating clubs (49%) were sponsored by the alcohol industry and one-quarter (27%) were sponsored by the fast food industry. In multivariate analyses, the odds of alcohol sponsorship among rugby league clubs was 7.4 (95%CI: 1.8-31.0, p=<0.006) that of AFL clubs, and clubs located in regional areas were more likely than those in major cities to receive fast food industry sponsorship (OR= 9.1; 95%CI: 1.0-84.0, p=0.05). The majority (78-81%) of club representatives and parents were supportive of restrictions to prohibit certain alcohol sponsorship practices, but a minority (42%) were supportive of restrictions to prohibit certain fast food sponsorship practices. CONCLUSIONS: Large proportions of community sports clubs with junior members are sponsored by the alcohol industry and the fast food industry. There is greater acceptability for prohibiting sponsorship from the alcohol industry than the fast food industry. Implications for public health: Health promotion efforts should focus on reducing alcohol industry and fast food industry sponsorship of junior sports clubs.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".