Work Related Musculoskeletal Pain in Golf Caddies—Johannesburg, South Africa
Bibliographic record
Abstract
Golf is an important and growing industry in South Africa that currently fosters the creation of an informal job sector of which little is known about the health and safety risks. The purpose of the study is to investigate the prevalence and significance of musculoskeletal pain in male caddies compared to other golf course employees while holding contributing factors such as socioeconomic status, age, and education constant. Cross-sectional data were collected and analyzed from a convenience sample of 249 caddies and 74 non-caddies from six golf courses in Johannesburg, South Africa. Structural interviews were conducted to collect data on general demographics and musculoskeletal pain for two to three days at each golf course. On average, caddies were eight years older, had an income of 2880 rand less a month, and worked 4 h less a shift compared to non-caddies employed at the golf courses. Caddies were approximately 10% more likely to experience lower back and shoulder pain than non-caddies. Logistic regression models show a significantly increased adjusted odds ratio for musculoskeletal pain in caddies for neck (3.29, p = 0.015), back (2.39, p = 0.045), arm (2.95, p = 0.027), and leg (2.83, p = 0.019) compared to other golf course workers. The study findings indicate that caddying, as a growing informal occupation is at higher risk for musculoskeletal pain in caddies. Future policy should consider the safety of such a vulnerable population without limiting their ability to generate an income.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".