Associations between markers of health and playing golf in an Australian population
Bibliographic record
Abstract
OBJECTIVE: To investigate associations between markers of health and playing golf in an Australian population. METHODS: Secondary analysis of data from the Australian National Nutrition and Physical Activity Survey to compare selected health outcomes between golfers (n=128) and non-golfers (n=4999). RESULTS: Golfers were older than non-golfers (mean±SD 57.7±14.2 years, 48.5±17.6 years, p<0.05). A higher proportion of golfers were overweight or obese compared with non-golfers (76% vs 64%, p<0.05), and golfers were more likely to have been diagnosed with ischaemic heart disease (IHD) at some time in their life (OR 2.8, 95% CI 1.0 to 7.8). However, neither the risk of being overweight or obese (OR 1.4, 95% CI 0.9 to 2.2) or having been diagnosed with IHD (OR 2.1, 95% CI 0.8 to 5.8), were significant after controlling for age. Golfers were more physically active than non-golfers (8870±3810 steps/day vs 7320±3640 steps/day, p<0.05) and more likely to report high health-related quality of life (HRQoL) than non-golfers (OR 1.8; 95% CI 1.0 to 3.3), but not after adjusting for physical activity (OR 1.4, 95% CI 0.9 to 2.2). CONCLUSION: Compared with non-golfers, golfers were more likely to be overweight or obese and to have been diagnosed with IHD, but not after adjusting for golfers being older. Golfers were more likely to report a higher HRQoL, but not after adjusting for golfers being more physically active. There may be an association between golfers being more physically active than non-golfers and reporting a higher HRQoL.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".