MOTOR PERFORMANCE OF BRAZILIAN FEMALE ARTISTIC GYMNASTS: INSIGHTS VIA MULTILEVEL ANALYSIS
Bibliographic record
Abstract
The aim of this research was to investigate individual and club-level variables that explain individual differences in gymnasts´ motor performance (MP). The sample was comprised of 249 female gymnasts (68 elite; 181 non-elite), aged between 9 and 20 years, split into four age categories: 9-10 years (n=98); 11-12 years (n=72); 13-15 years (n=64), and 16 and above (n=15). Gymnasts were from 26 Brazilian clubs, from six different states. The Talent Opportunity Program physical ability total test score was used to assess gymnasts’ MP, based on a battery of seven tests: handstand hold, cast, rope climb, press handstand, leg flexibility, leg lift, and 20 meter sprint. Anthropometric, body composition, biological maturation, and training history data were also collected, as were club dimensions, infrastructures, competitions, manpower, and availability of selection/talent programs. Data were analyzed using a multilevel modelling approach. Individual gymnast’ characteristics explained 39% of physical ability score variance from which 32% was related to the independent effects of age, competitive level, fat free mass, occurrence of menarche, and trainings hours per week (p<0.05). Club characteristics explained 61% of gymnasts’ total variance in physical ability score; 96% of this amount was related to club dimension, manpower, and talent program. These results reinforce the relevant role of the contextual effects and highlight the need to invest in club infrastructures: ideally in coaches’ expertise and effective selection programs. Such investments should enable the enhancement and development of a gymnast’s careers during their lifetime involvement in training and competition.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".