The Comparative Mental Health Responses Between Post-Musculoskeletal Injury and Post-Concussive Injury Among Collegiate Athletes: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The average annual national estimate of injuries sustained by collegiate athletes is 210,674, which encompasses both those of a musculoskeletal and a concussive nature. Although athletic injuries are sustained through physical means and produce physical symptoms, sports-related injuries may be a stressor among athletes that is related to mental health. PURPOSE: The purpose of this systematic review is to summarize existing literature describing mental health responses in collegiate athletes with a concussion compared to those with a musculoskeletal injury. STUDY DESIGN: Systematic Review. METHODS: Systematic searches of PubMed, CINAHL, Scopus, ProQuest, and SportDiscus were completed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were utilized. Methodological quality was assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Tool. Data extracted from the included articles included the study design, number of participants, type of injury, sex, age, sport participation, outcome measures, and time to return to play. RESULTS: A total of six articles were included. Peak depressive symptoms in athletes who sustain a concussion or musculoskeletal injury occur within one-week post-injury. No significant differences between the concussive and musculoskeletal groups anxiety scores were found at baseline or at each follow-up session. Athletes from both groups were found to be returning to their respective sports with anxiety scores representative of clinical anxiety. CONCLUSION: Similar trends in depressive and anxiety symptoms at various time points post-injury were observed in athletes with both musculoskeletal and concussive injuries. This study identified that athletes were returning to play before their psychological symptoms had returned to their baseline. LEVEL OF EVIDENCE: 2a.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".