Exploring challenges and opportunities related to the identification of mild traumatic brain injury and concussion in rehabilitation trauma inpatients: a qualitative study
Bibliographic record
Abstract
PURPOSE: To examine the perspectives of healthcare providers regarding missed mild traumatic brain injury and concussion (mTBI/C) diagnosis in trauma populations in rehabilitative and other post-acute settings. MATERIALS AND METHODS: An inductive qualitative study design was undertaken to obtain insights from healthcare providers across care settings (e.g. acute care, rehabilitation) about mTBI/C. A total of 20 healthcare providers took part in semi-structured interviews. Data were analyzed using codebook thematic analysis. RESULTS: Four main themes were identified from the data: (1) the prevalence of missed mTBI/C; (2) the challenges of identifying and managing mTBI/C; (3) current approaches to identifying and managing mTBI/C, and; (4) recommendations for improving mTBI/C identification and management. CONCLUSION: Our qualitative research sheds light on the complexities encountered by healthcare providers with regard to identifying and managing mTBI/C at a patient, practice and system-level. The findings from this work highlighted the variability in diagnostic methods and approaches across care settings and disciplines, emphasizing the need for standardized approaches and enhanced interdisciplinary communication to optimize mTBI/C care.
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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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".