Technology Based Community Navigation Solutions for Individuals with Acquired Brain Injury and Executive Functioning Deficits
Bibliographic record
Abstract
One Area of function that has been identified as particularly difficult for individuals with acquired brain injury (ABI) is that of community mobility and transportation. The aim of this project was to create an instructional manual describing a two-day training program to assist caregivers of individuals with ABI and resulting executive dysfunction. Caregivers can implement the training program to aid their care recipients in identifying and using technology devices to aid in community mobility. The first day focuses on assisting individuals with ABI in identifying cognitive strategies and appropriate technology for navigating public transportation. Caregivers and their care recipients also learn facts about public transportation, along with solutions to common barriers, such as route finding and appropriate behavior while on the bus. During the second day, caregivers guide participants in using technology to navigate King County public transit while taking a trip on the bus. After completing the training program, caregivers can be better prepared to teach individuals with ABI how to navigate public transportation with technology and know the next steps for assisting their care recipients to use public transportation. With assistance from caregivers, the participant can learn skills necessary to navigate public transportation with technology, thereby increasing their independence and sense of self-efficacy with community mobility.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".