An exploratory investigation into the reasons why older people play golf
Bibliographic record
Abstract
Purpose: Participation in organised sport is declining. For older Australians, golf is the most popular organised sport, but why golf is popular in this age group is unclear. This study explored why older adults play golf, and the perceived benefits they obtain from doing so. Method: A qualitative descriptive approach directed the study, involving five focus groups with male and female regular golfers (N = 31, aged 55–74 years) from private/semi-private metropolitan and country/regional golf clubs. Reasons for, and perceived benefits of, golf participation were explored. Results: Reasons viewed as unique to golf included a relatively low physical demand allowing play into older age, providing an opportunity to compete (due to the handicap system providing a level playing field) and providing opportunity to exercise without it feeling like exercise. Reasons for participation common to other sports/activities were opportunities for social and community engagement, time for self and time spent with others, and benefits for physical, cognitive, and mental health. Conclusion: These findings provide new insights into the reasons why older adults play golf that might start to explain the relative popularity of golf as a sport for older people. The results may inform recruitment and marketing strategies for the golf industry, whilst contributing to the knowledge base of factors that influence participation in sport for older adults. The study also provides understandings that could be used to guide further research using both qualitative and quantitative methods of inquiry.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".