Effects of Plyometric Training on Latin Dance-Specific Motor Skills in University Student Dancers: A Pilot Study
Bibliographic record
Abstract
This study evaluated the effects of short-term plyometric training on dance-specific skills in university Latin dancers. Fourteen experienced dancers were randomized to an experimental group (EG: plyometric training, n=6) or control group (CG: standard training, n=8), completing three weekly sessions for 8 weeks. Balance, footwork, and spins & turns were assessed pre- and post-intervention using generalized estimating equations (GEE). Significant Group × Time interactions emerged for balance (Wald χ² = 7.303, p = 0.007) and footwork (Wald χ² = 6.526, p = 0.011), favoring the EG. The EG demonstrated large improvements in balance (d = 1.909), footwork (d = 2.448), and spins & turns (d = 1.849), while the CG showed smaller gains in footwork (d = 1.677) and spins & turns (d = 1.680) but not balance (d = 0.410). Post-intervention, the EG outperformed the CG across all skills (p < 0.05), with gender significantly influencing footwork outcomes (p < 0.001). These findings support plyometric training as an effective method for enhancing Latin dance performance, particularly for balance and footwork. The results provide empirical justification for integrating sport-science conditioning into dance curricula.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".