Development and Reliability of a 7×15m Repeated On-Ice Sprint Test for Female Ice Hockey Players
Bibliographic record
Abstract
International Journal of Exercise Science 14(6): 666-676, 2021. The purpose of this investigation was to design and examine the reliability of a 7×15m repeated on-ice skating sprint test for female ice hockey players. Seventeen women ( ± SD age, height and body mass = 21 ± 2 years, 166.2 ± 6.4 cm and 61.9 ± 7.7 kg, respectively) completed 7 consecutive on-ice sprints of 15m repeated every 15s. Two trials of the test were performed on the same day and then repeated on a different day approximately 1 week later for a total of 4 trials. The fastest 15m time, mean time for 7 sprints and total sprint time collapsed across all 4 trials was 2.96 ± 0.12s, 3.05 ± 0.13s and 21.35 ± 0.89s, respectively. There were no significant differences between trials for any variable. Typical error (TE), coefficient of variation (%CV) and intra-class coefficients (ICC) for the fastest 15m time, mean of 7 sprints, and total time were ICC = 0.77, TE = 0.06s and %CV = 2.1; ICC = 0.91; TE = 0.04s and %CV = 1.4; and, ICC = 0.91; TE = 0.29 and %CV = 1.4 for all 4 trials, respectively. Players in the forward position had a faster mean 15m time and lower total time compared to those in the defensive position (p < 0.05). These findings show that a 7×15m repeated on-ice sprint test for varsity women ice hockey players was reliable. It was also found that forwards had a better mean of 7 sprint time and faster total time compared to players in the defensive position.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".