The Impact of a Recent Concussion on College-Aged Individuals with Co-Occurring Anxiety: A Qualitative Investigation
Bibliographic record
Abstract
College-aged individuals with anxiety are vulnerable to developing persistent concussion symptoms, yet evidence-based treatments for this population are limited. Understanding these individuals' perspectives is critical for developing effective interventions. We conducted qualitative interviews with 17 college-aged individuals (18-24 years old) with a recent (≤10 weeks) concussion and at least mild anxiety (≥5 on the GAD-7 questionnaire) to understand the life impact of their concussion. We identified 5 themes: (1) disruption to daily activities (e.g., reduced participation in hobbies and physical activity); (2) disruption to relationships (e.g., reduced social engagement, feeling dismissed by others, stigma, and interpersonal friction); (3) disruptions in school/work (e.g., challenges participating due to light sensitivity, cognitive or sleep disturbance, and related emotional distress); (4) changes in view of the self (e.g., feeling "unlike oneself", duller, or more irritable), and (5) finding "silver linings" after the injury (e.g., increased motivation). Concussions impact the lives of college-aged individuals with co-occurring anxiety in a broad range of domains, many of which remain largely neglected in standard concussion clinical assessment and treatment. Assessing and addressing these issues has the potential to limit the negative impact of concussion, promote recovery, and potentially help prevent persistent concussion symptoms in this at-risk population.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".