Patient perceptions of persistent symptoms after mild traumatic brain injury and their influence on mental health treatment-seeking: a grounded theory study
Bibliographic record
Abstract
PURPOSE: Mental health conditions after mild traumatic brain injury (mTBI) are common and can complicate injury outcomes, but are under-treated. According to the Common Sense Model of Self-Regulation, the way patients perceive their health conditions can influence the way they manage them, including if, when, and how they seek treatment. This study explored how individuals perceive persistent symptoms after mTBI, in order to develop a grounded theory about what motivates and demotivates them to seek mental health treatment after their injury. METHODS: Seventeen adults experiencing persistent symptoms after mTBI participated in semi-structured interviews, which were analyzed using constructivist grounded theory. RESULTS: An explanatory model of patient perceptions was developed with three interrelated categories: (1) Symptom persistence and uncertainty result in life challenges; (2) Self-advocating for answers through extensive treatment-seeking; preference for biomedical treatment; (3) Mental health problems are caused by symptom persistence, therefore mental health treatment is supplementary. CONCLUSION: Findings suggest a potential barrier to seeking mental health treatment during complicated mTBI recovery: if patients do not consider mental health as an important cause of their ongoing symptoms, they may feel less motivated to prioritize mental health treatment.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".