Optimizing the Benefits versus Risks of Golf Participation by Older People
Bibliographic record
Abstract
Currently a strong emphasis is being placed in North American public health messages on the value of an active lifestyle for all age segments, including older persons. However, seniors do not usually take up physical activities, even though they often have extensive leisure time. Thus the purpose of this paper is to review current knowledge regarding the key health issues for physical therapists to consider when dealing with an older person who wishes to participate fully in an active sport. We have chosen the example of golf because of its popularity among seniors, as well as its usefulness in illustrating both the overall benefits and risks of participation. Although playing golf provides a moderate intensity exercise stimulus for seniors, musculoskeletal injuries can also result from unsafe participation, as can the aggravation of pre-existing musculoskeletal problems. Strategies for targeted management of the senior golfer's typical concerns are summarized into 4 categories consisting of: injury rehabilitation coordinated by therapists, warm up routines; club-fitting/coaching on proper technique, and pre-season conditioning programs. Educational programs for older people regarding the benefits of physical activity should also include information about injury prevention strategies that enhance long-term participation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".