Differential Emotional Responses of Varsity Athletes to Concussion and Musculoskeletal Injuries
Bibliographic record
Abstract
OBJECTIVE: To determine if athletes with concussion and those with minor musculoskeletal injuries experienced differential emotional response to injury. DESIGN: A prospective longitudinal cohort study. SETTING: University of Toronto, Ontario, Canada. PARTICIPANTS: Thirty-four injured athletes from Canadian Interuniversity Sport (CIS) and 19 healthy, physically active undergraduate students participated in the study. INTERVENTION: All participants completed the Profile of Mood States (POMS; short version) on 3 nonconsecutive days during a 2-week period after a baseline test. MAIN OUTCOME MEASURES: Emotional responses were assessed using the POMS. The 7 main outcome measures assessed by POMS were tension, depression, anger, vigor, fatigue, confusion, and total mood disturbance. RESULTS: After injury, concussion produced an emotional profile characterized by significantly elevated fatigue and decreased vigor. In contrast, athletes with musculoskeletal injuries displayed a significant increase in anger that resolved to a pre-injury level within 2 weeks. CONCLUSIONS: The results revealed that both injured groups experienced emotional disturbance after injury. More importantly, the findings strongly suggest that the emotional reaction after concussion is different from that of musculoskeletal injury. Therefore, we concluded that assessing emotional reactions to concussion is particularly important and recommend that sports medicine professionals assess and monitor emotional functioning as well as somatic complaints and neurocognitive changes during recovery.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".