Clinician Reports of Self-Awareness after Traumatic Brain Injury: A Retrospective Chart Review
Bibliographic record
Abstract
Abstract Background: Impaired self-awareness (i.e., lack of insight) is experienced by most individuals after a moderate to severe traumatic brain injury (TBI). These individuals do not recognize their abilities and limitations which can negatively impact daily life and function. Although there are evidence-based approaches to improve self-awareness after TBI, it is not known how clinicians respond and address this impairment in an inpatient rehabilitation setting.Objective: To examine how clinicians report, assess, and provide intervention for impaired self-awareness after TBI.Methods: A retrospective chart review was conducted on interdisciplinary rehabilitation clinician entries for individuals with TBI (n=67) who received inpatient rehabilitation within a five-year period (2014-2019). A reflexive thematic analysis approach was used to analyse the data.Results: Three themes were generated to explore clinician responses to their clients’ impaired self-awareness: 1) ‘recalling and understanding’ described clinicians observing client behaviours and expressions of self-awareness, 2) ‘applying and analyzing’ identified clinicians providing relevant tasks and advice to clients, and 3) ‘evaluating and creating’ described clinicians actively interacting with clients by providing feedback, guided prompts, and a follow-up plan. Conclusion: Clinicians described varied responses to clients’ impaired self-awareness after TBI. Findings may help to develop research priorities and integrated knowledge translation initiatives to increase evidence-based practice for impaired self-awareness after TBI.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".