Gender Differences in Psycho-affective Outcomes of Concussion in University Athletes
Bibliographic record
Abstract
BACKGROUND: Concussion or mild traumatic brain injuries are known to cause alterations in psycho-affective health (Jorge, 2004). Relative to males, females are thought to be at greater risk for concussion and report more intense symptoms following concussion (Covassin, 2007). Despite this, few studies comprehensively evaluated psycho-affective health between males and females beyond the acute phase of injury (Barnes, 1998; Covassin, 2007). PURPOSE: To assess longitudinally the influence of gender on psycho-affective health following concussion. METHOD: 30 collegiate athletes (14 females, age = 20.79 ± 1.37; 16 males, age = 20.93 ±1.12) completed the Beck’s Depression Inventory-II (BDI-II) and the Profile of Mood States (POMS) at 7 and 30 days following a concussion. RESULTS: On the POMS subscales, all athletes had greater anger (p=0.05), vigor (p=0.03), fatigue (p=0.01), confusion (p=0.01) and total mood disturbances (p=0.03) at day 7 compared to day 30 post-injury. However, analyses failed to reveal any gender differences for any of the POMS subscales at either time point. All athletes also exhibited greater intensity of depressive symptoms on the BDI-II (p<0.01) at day 7 compared to day 30. However, at day 7, female athletes had significantly greater scores on the BDI-II than male athletes (p=0.05), but no gender difference was found at 30 days post-injury. CONCLUSION: The current results suggest that gender differences in psycho-affective outcomes following concussion are selective to depressive symptoms. Further these differences appear relegated to the acute phase of injury. Thus time since injury, not gender, appears to be the most important factor moderating the intensity of psycho-affective symptoms following a concussion.
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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".