Return to work following traumatic brain injury: An exploration of using the Brannagan Executive Functions Assessment to increase self-awareness in practice
Bibliographic record
Abstract
Aim and Background: The Brannagan Executive Functions Assessment is an occupation-based approach for increasing self-awareness. This study explores the application of the Brannagan Executive Functions Assessment in traumatic brain injury return to work intervention. It describes changes in self-awareness, goal achievement and perspectives of occupational therapy and people with brain injury. Methods: Case study design using a mixed-methods approach: with pre-post evaluation of outcomes and semi-structured interviews. Participants with traumatic brain injury completed the assessment with two occupational tasks related to return to work. Primary outcomes were self-awareness (Self-awareness Deficits Interview and Awareness Questionnaire) and Goal performance/satisfaction using the Canadian Occupational Performance Measure. Mood was a secondary outcome. Analysis was descriptive for quantitative data, thematic for qualitative data. Results: Two participants with brain injury, two support people and one occupational therapist were recruited. One participant with brain injury had an increase in self-awareness scores. Brain injury participants and the occupational therapist identified benefits including an increased awareness of needing to incorporate planning to meet goals. Conclusion: The Brannagan Executive Functions Assessment offers benefits to practice in brain injury rehabilitation, facilitating a structured occupational approach to self-awareness and participation in meaningful return to work tasks.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".