Physical activity participation constraints among athletic trainers: A profession based assessment
Bibliographic record
Abstract
Abstract Despite well‐documented health benefits, physical activity participation rates remain low. Leisure constraints theory can provide insights into understanding non‐participation in physical activity. One hundred fifty‐three certified athletic trainers from the U.S. Big‐10 and PAC‐10 conferences were surveyed to understand physical activity participation and leisure constraints among this group. Hierarchical regression analysis was performed to examine the effect of gender, age and leisure constraints on physical activity participation. Findings suggest less than ideal physical activity participation, some level of dissatisfaction with physical activity participation, and high mean Body Mass Index level (BMI) among the sample. Although structural constraints significantly and negatively predicted actual physical activity participation, the leisure constraints model did not explain the variance in physical activity participation. Athletic trainers are trained to be physically fit, maintain healthy lifestyles and reduce constraints for other, but ultimately may sacrifice their own health in the process. Résumé Malgré plusieurs avantages documentés, le taux de participation d'activité physique reste faible. La théorie des contraintes de loisirs peut fournir une compréhension plus approfondie sur le manque d'activité physique. Cent cinquante‐trois entraîneurs sportifs certifiés des conférences du ≪ Big 10 ≫ et le ≪ PAC‐10 ≫ aux États‐Unis ont été enquêté pour mieux comprendre l'activité physique et les contraintes de loisirs au sein de ce groupe. L'analyse de régression hiérarchique a été réalisée pour examiner l'effet du sexe, l'âge et les contraintes sur l'activité physique. Les résultats suggèrent que la participation idéale de l'activité physique n'existe pas avec ce group, il existe aussi un certain niveau d'insatisfaction avec la participation en activité physique, et un taux de niveau élevé de l'indice de masse corporelle. Bien que les contraintes structurelles puissent prédire une participation active, les contraintes du loisir n'expliquent pas la variance dans l'activité physique. Les entraîneurs sportifs sont formés pour être en bonne forme physique, ' pour maintenir un mode de vie sain, et de réduire les contraintes pour les autres, mais ils doivent sacrifier leurs propre santé et leurs propre bien‐être pour atteindre les buts de leurs clientèles. Keywords: athletic trainersleisure constraintsphysical activity participationMots‐clés: entraîneurs sportifsles contraintes de loisirsparticipation à l'activité physique
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".