Living in the Shadows: My Personal Perspective With Mental Health After a Concussion
Bibliographic record
Abstract
BACKGROUND: Concussions are a common injury, with many experiencing mental health related symptoms after injury. Despite the persistence of these symptoms, it is not well detailed the impacts of anxiety and depressive-like symptoms. AIM: This lived experience narrative recounts the authors lived experience with multiple concussion injuries and mental health symptoms. METHODS: The author used a narrative approach to detail the multiple experiences with concussion. The author reflected on these experiences, drawing on their experience as a registered nurse. FINDINGS: The narrative reveals persistent concussion symptoms are multifactorial. It illustrates how the interdisciplinary mental health care team can benefit concussion patients experiencing anxiety and depressive-like symptoms. DISCUSSION: The author reflects on their journey and calls for a shift to a more person-centred approach in concussion care. They also highlight areas for improvement in mental health services. Lastly, future directions for mental health nursing are provided. CONCLUSION: This narrative highlights the anxiety and depressive symptoms experienced after concussion. By incorporating mental health nurses within concussion care, mental health issues can be addressed in a timely and approriate manner.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.020 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".