Women in university hockey demonstrate knowledge discordant with attitudes regarding concussions
Bibliographic record
Abstract
Objective To examine the knowledge and attitudes about sport-related concussion among university athletes. Design Cross-sectional study. Setting Validated survey instrument. Subjects Seventy-two university soccer, hockey, and basketball athletes (28 males, 44 females). Main outcome measures Total athlete knowledge scores and attitude scores were measured and average scores were also determined for each sport, sex, and combinations of sport and sex. Results The average athlete knowledge total score was 28.9 ± 2.8 ( SD ) out of a possible 35, and the average attitude total score was 76.4 ± 11.8 ( SD ) out of a possible 98. Correlations between athlete knowledge scores and athlete attitude scores were positive in the all male athlete sample ( r = 0.09, n = 28, p = .65). The same correlation in the all female athlete sample was negative ( r = −.33, n = 44, p < .05). Sixteen athletes suffered a total of 21 concussions in the 2015–2016 playing season. Conclusions The present sample of university athletes have higher knowledge scores but poorer attitude about concussions. Knowledge regarding concussion did not translate into improved attitudes particularly for female hockey athletes. Coaches should consider concussion education focused on communicating facts, as well as contextual interventions and practical athlete responses to injury. Interventions should consider the differences between male and female athlete reporting tendencies in this demographic. Appropriate detection and reporting systems may help to overcome poor reporting behaviors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".