Cross-cultural adaptation and validation of the Portuguese survey of musculoskeletal conditions, playing characteristics and warm-up patterns of golfers
Bibliographic record
Abstract
BACKGROUND: The University of Western Ontario Questionnaire for Musculoskeletal Conditions in Senior Golfers (MSK Golfers) was developed in Canada because of a lack of knowledge concerning musculoskeletal conditions directly related to golf play and warm-up, although the high injury incidence in golf practice. This lack of epidemiological measures also exists for the Portuguese golf population. OBJECTIVE: The purpose of this study was to translate and cross-culturally adapt the MSK Golfers questionnaire into Portuguese and to test its construct validity and reproducibility. METHODS: The MSK Golfers was translated from English to Portuguese and tested for psychometric properties. Sixty-one golfers, aged between 14 and 70 years and with at least 1 year of practice in golf, were recruited. The validity of the MSK Golfers was assessed by evaluating data quality (missing, floor and ceiling effects). Reproducibility analysis included intra-class correlation coefficients (ICC) (2,1) and Cohen's Kappa coefficient. RESULTS: The ICC values for continuous items ranged from 0.634 to 0.998 with the exception of one item on golf activity. Kappa statistics for the categorical items ranged between 0.714 and 1.00. CONCLUSIONS: The Portuguese version of the MSK Golfers, including playing characteristics and warm-up patterns of golfers, showed a high reliability for a golfing population with an age range of 14 to 70 years.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".