Use of the Dynamic Interactional Model in Self-Care and Motor Intervention After Traumatic Brain Injury: Explanatory Case Studies
Bibliographic record
Abstract
PURPOSE: The highest proportion of traumatic brain injuries (TBIs) occurs among adolescents. This study examines the effectiveness of a therapeutic protocol for rehabilitation of adolescents with TBI. This protocol is based on Toglia's (1998, 2005) Dynamic Interactional Model and Expanded Awareness Model (Toglia & Kirk, 2000). METHOD: Explanatory case studies presenting 2 adolescents (ages 16-17) with mild to moderate TBI are combined with qualitative and quantitative data assessing self-care, mobility, and graphomotor abilities as well as deficit awareness in these domains. Outcome measures include the FIM, the Canadian Occupational Performance Measure, the Computerized Penmanship Object Evaluation Tool, and the Awareness of Mobility Deficits Questionnaire. Graphic data analysis compared outcome measures before, during, and after intervention. RESULTS: The Dynamic Interactional Model was effective in improving self-care, mobility, and graphomotor abilities and identified awareness of deficits in these domains. CONCLUSION: These research findings contribute to our theoretical knowledge in rehabilitation and promote adopting this approach for rehabilitation of adolescents with 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 | 0.009 | 0.017 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".