Influence of different verbal cue types on countermovement jump performance in youth female athletes
Bibliographic record
Abstract
Although external (EXT) verbal cues have been shown to improve jump performance, limited research has been conducted in youth athletes. Therefore, the purpose of this study was to investigate how different EXT verbal cue types influenced countermovement jump (CMJ) performance in youth athletes. Ten young trained female athletes (16.80 ± 0.60 years) performed the CMJ using four different EXT verbal cues including external-distal (DIST), external-proximal (PROX), analogy (ANA) and holistic (HOL) as well as a neutral cue (CON). Jump height, reactive strength index modified (RSI MOD ), countermovement depth, jump time and force-time measures from eccentric and concentric CMJ subphases were measured. No significant differences between cue types were found for RSI MOD . ANA resulted in significantly higher jump height compared to CON ( g = 0.37). PROX significantly increased jump time ( g = 0.60), concentric time ( g = 0.47) and braking time ( g = 0.52), as well as significantly decreasing eccentric braking mean force ( g = 0.43) and eccentric braking rate of force development ( g = 0.59) compared to CON. Countermovement depth significantly increased when using PROX ( g = 0.60), HOL ( g = 0.46) and ANA ( g = 0.51) compared to CON. These results suggest that compared to a neutral cue, EXT verbal cues result in similar CMJ performance in trained youth athletes. However, PROX verbal cues appear to result in reduced performance in many CMJ force-time measures.
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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 | 0.000 | 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) | 0.001 | 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".