A Different Perspective for Coaching and Training Education According to Score Changes During Rhythmic Gymnastics European Championships
Bibliographic record
Abstract
Rhythmic gymnasts repeat elements thousands of times which may put a risk on gymnasts’ health. It is necessary to protect the current and future health conditions of young gymnasts, especially in the growth process. There is a lack of knowledge about training education on rhythmic gymnastics. To suggest innovative changes, the current study aimed to analyze the scores (D, E, and total scores) of the first 24 gymnasts competing in 34th and 36th Rhythmic Gymnastics European Championships (ECh). Research data were collected from 24 rhythmic gymnasts’ scores, from the 34th ECh and 36th ECh. Difficulty (D), Execution (E), and total scores for hoop, ball, clubs, ribbon were analyzed. Conformity of data to normal distribution was assessed with the Kolmogorov-Smirnov test. Variables with normal distribution were compared by one-way analysis of variance (ANOVA)/independent samples t-test and for variables not fitting normal distribution, Mann Whitney U/Kruskal Wallis H test was used. The main findings of the current study were that D scores between 2018 and 2020 increased approximately 4.18 points (p<0.001) while E scores showed no significant changes (p>0.05). In all apparatus total scores increased +15.39 (p<0.001). As these increases seem to be in the faith of gymnasts to get higher results, it is obvious that gymnasts are forced to have a higher number of elements in their routines. Coaches should be informed about new training models and coaching education systems. In this way, they will be able to support their gymnasts in all ways (not only with performance development but also with recent updates on training educations).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".