A cross-cultural adaptation of the university sport experience survey
Bibliographic record
Abstract
Qualitative methods dominate Positive Development (PD) research. A need for reliable and valid quantitative measurement tools exists (MacDonald & McIsaac, 2016). Rathwell and Young (2016) created the University Sport Experience Survey (USES), which assesses PD in university sport. Although the psychometric properties of USES were confirmed, its validity may be confined to Canadian settings. The purpose of this study was to assess the fit and factor structure of a Portuguese version of the USES (USES-BR) with a sample of university athletes from all regions of Brazil (N = 1021; Male = 516, Female = 496; Mage = 22, SD = 3.17). The cross-cultural adaptation of the USES involved back-translation (two translators and two back translators) and content validity analysis by a committee made up of PhDs and bilingual professors. The USES-BR presented acceptable coefficients (CVC> 0.80 – Hernandez-Nieto, 2002) for clarity of language, practical relevance, and theoretical relevance. The item-dimension agreement coefficient was considered substantial (Kappa = 0.69 – Landis; Koch, 1977). The fit and factor structure were assessed using CFA and results showed good fit: CFI = .867, SRMR = .042, RMSEA = .041 (90% CI = .039 – .042), X² (953) = 2541.585, p < .001, and X²/df = 2.667. Our results suggest this confirmed model, containing Portuguese translated items, has strong factorial validity for assessing developmental outcomes of university-aged student-athletes in Brazilian university contexts. The current results support the external validity of the USES, and offer evidence of the first validated Portuguese assessment tool for assessing PD in university sport.
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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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".