The Level of the Functional Movement Screen Among Traditional Dancers
Bibliographic record
Abstract
Movement assessments are commonly used to assess athlete’s risk of injury as well as basic and specific skill movement patterns; however, dance is identified to be differing from sports because the average dancer’s training load is higher than the athletes. This study aims to identify the difference in the Functional Movement Screen (FMS) level among traditional dancers in Malaysia. A quasi-experimental study design was adopted, which involved 66 dancers (M = 33; F = 33). The study comprised traditional dancers from three ethnic backgrounds, namely, Malay, Chinese and Indian. The descriptive analysis described the level of the dancers’ FMS, as follows: Malay (M = 16.18, SD = 2.062), Chinese (M = 18.50, SD = 1.102), Indian (M = 18.23, SD = 1.445). The ANOVA analysis found a significant difference in the FMS scores among all three groups of dancers, F (2,63) = 14.026, p >.000. The deep squat, hurdle step, shoulder mobility, active straight leg raise and trunk stability tests for push-up indicated a significant difference, whereas the inline lunges test and rotational stability tests showed no significance difference. However, the Post Hoc analysis showed no significant difference between the Chinese and Indian dancers. It can be concluded that there is a difference in FMS scores between Malay, Chinese and Indian dancers. FMS may be a useful tool to help identify dancers about the risk of injury and improve their movement quality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".