Impact Of Workload On Female Ice Hockey Player Complaints, Time-loss, And Perceived Performance
Bibliographic record
Abstract
Associations between athlete load and injury risk have been investigated in field-based sports, with variability depending on specific measures used to assess exposure. However, work examining this relationship in ice hockey is currently limited, especially in female players. PURPOSE: To assess the association between female ice hockey player external and internal loads and the odds of self-reported complaints of injury and pain, time-loss from sport, and perceived effect on performance. METHODS: Twenty-three female university ice hockey players’ (19.9 ± 1.4 y, 68.2 ± 7.3 kg, 167.7 ± 5.6 cm) external (time-on-ice (TOI), skating distance, time spent skating in speed zones, and number of skating events measured via local positioning system) and internal (training impulse (TRIMP) and sessional rate of perceived exertion (sRPE)) loads were measured for an entire season, which were separated into low, low-medium, medium-high, and high load quartiles for analyses. Players’ self-reported injuries and pain, time-loss from sport, and perceived effect on performance prior to each practice and game. Using binomial logistic regression, crude odds ratios (OR) were calculated to assess weekly cumulative load measures and the odds of the various outcomes during that week. RESULTS: Players reported a total of 57 injury or pain complaints over the season. There were significant increases in the odds of a complaint within the low-medium (OR = 2.16, p = 0.049), medium-high (OR = 2.39, p = 0.03), and high (OR = 2.16, p < 0.05) load groups for TOI, and the high load group (OR = 2.22, p = 0.04) for TRIMP compared to the low load group. Interestingly, high load groups for TOI (OR = 0.18, p = 0.03), sRPE (OR = 0.22, p = 0.02), skating distance (OR = 0.16, p = 0.02), time spent in very low to high skating speed zones (OR = 0.16-0.30, p < 0.05), number of decelerations (OR = 0.31, p < 0.05) and skating transitions (OR = 0.09, p = 0.02) had a significant protective effect against time-loss from sport compared to the low load group. Apart from TRIMP (OR = 2.35, p = 0.03), high loads did not increase the odds of players reporting a perceived effect on their performance. CONCLUSION: Female ice hockey players who had high loads in various measures had greater odds of reporting complaints. However, these findings do not translate to time-loss or perceived effect on performance. PepsiCo and Mitacs
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".