Understanding the Barriers of Implementing a Self-Awareness Assessment in Occupational Therapy Practice within a Brain Injury Population: An Exploratory Study
Bibliographic record
Abstract
Background: Self-awareness is seldom formally assessed by occupational therapists among individuals with traumatic brain injury (TBI). However, impaired self-awareness is prevalent and has a significant impact on rehabilitation outcomes. There is a need to understand clinician perspectives on self-awareness assessments and promote evidence-based practice in clinical settings. Aims: (1) Explore how an education session impacts knowledge and use of self-awareness assessments in occupational therapists working with people with TBI; (2) Understand the barriers that occupational therapists experience when assessing self-awareness in clinical practice. Materials and Methods: A single-group pre-post session design with an integrated knowledge translation approach was used. Occupational therapists working in neurorehabilitation were recruited from two rehabilitation centres through convenience sampling. Participants completed questionnaires before, after, and three months following an education session about the Self-Awareness of Deficits (SADI) assessment. Results: 14 occupational therapists participated in this study. A statistically significant increase in knowledge and confidence in using the SADI was observed both post-session and at 3-month follow-up. Conclusion: . The barriers identified in this study can provide insights for knowledge translation across clinical contexts.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".