Tailoring cognitive assistance for individuals with a traumatic brain injury using assistive technology for cognition: translating clinical reasoning into ontologies
Bibliographic record
Abstract
PURPOSE: Individuals with a traumatic brain injury (TBI) frequently require cognitive assistance to engage in complex activities, emphasising the need for personalised support to enhance their independence. This study explored how occupational therapists use evaluation results to tailor cognitive assistance and translated these results into clinical reasoning ontologies that could be integrated into assistive technologies for cognition (ATCs). MATERIAL AND METHODS: Fifteen occupational therapists and occupational therapy master's students participated in focus groups presenting case studies of clients with moderate to severe TBI. Participants were asked how they would adapt cognitive assistance provided during interventions based on evaluation results and client characteristics. Thematic analysis was employed. Clinical reasoning ontologies were developed in interdisciplinary team meetings. RESULTS: Analysing clients' behaviours and the assistance provided during an Activities of Daily Living evaluation, participants determined the minimum cognitive assistance required for clients to progress in their tasks. Therapists suggested using evaluation results to inform initial cognitive assistance while also considering task characteristics, the intervention session goal and the treatment approach. Adjustments were made to progress the assistance by observing difficulties and responses to assistance during both individual sessions and across multiple sessions. The ontology represents a customisable assistance model, allowing cognitive assistance to evolve according to the context and the user profile. CONCLUSION: This study presents ontologies reflecting the clinical reasoning used by occupational therapists to tailor cognitive assistance during interventions for individuals with a TBI. Further work is needed to enable the implementation of these clinical reasoning ontologies into ATCs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".