Effects of ice hockey facial protectors on the response time and kinematics in goal-directed tasks
Bibliographic record
Abstract
Ice hockey facial protectors are essential to prevent eye (and, in some cases, dental) injuries but must also not encumber vision and, in turn, playersapos; performance. The purpose of this study was to investigate the effects of three different facial protection conditions on temporal and kinematic parameters in a goal-directed pointing task: helmet (control), visor, and cage. Start and end target switches captured temporal estimates (reaction time (RT), movement time, (MT), and response time (RT+MT)), while a 13-light target array and 6-camera Vicon Mx system were used to collect upper-body kinematics data (head and thorax orientation, shoulder and elbow joint angles). Subjects recruited were 16 male and 12 female varsity ice hockey players ( n=28). Results demonstrated that, although kinematics remained largely unaffected, throughout the target array RTMT increased significantly with the cage (23 ms) as well as delayed initiation of head rotation for both the visor (14 ms) and the cage (18 ms). These differences may well represent a functional disadvantage to a player's performance given the dynamic open environment where multiple players contest for puck possession. It must be stressed that facial protectors still provide an effective form of protection and thus should still be worn at all levels of play. In summary, further research is warranted to achieve both optimal performance and safety.
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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) | 0.003 | 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".