Relação entre nível de aptidão física e motivação na prática do Futebol feminino
Bibliographic record
Abstract
Introduction: Among the various situations involved in sports, motivational techniques tend to improve the athlete's intrinsic and extrinsic motivation. Aim: To identify the relationship between physical fitness level and motivation in female football practice. Materials and Methods: Twenty-two healthy adolescent women with a mean age of 15.9 ± 1.3 years who participated in a women's football team for more than one year answered the Sports Motivation Scale (EME) -BR), which evaluates levels of intrinsic and extrinsic motivation. The physical fitness tests were also applied: body composition through the protocol of Pollock and Wilmore (1997); abdominal resistance; flexibility with the Wells Bank and classified according to the criteria established by Costa and Pires Neto (2009); the maximum oxygen consumption (VO2 max) was determined through the "Université de Montréal Track Test" (UMTT) field test; the grip strength was performed by means of a hydraulic dynamometer. Results: There is a close relationship between specific components of physical fitness and the motivation of female football players, with emphasis on the relationship between fat percentage and demotivation (r = 0.631 p = 0.00); flexibility and strength with intrinsic motivation (r = -0.533 p = 0.01; r = -0.423 p = 0.05); maximal aerobic velocity and intrinsic motivation (r = 0.506 p = 0.05). Conclusion: The level of physical fitness of football athletes is closely related to the observed motivation, indicating that the greater the body composition, the greater the demotivation; the greater the flexibility and strength, the lower the intrinsic motivation, and the better the aerobic capacity the greater the intrinsic motivation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".