Use of smartphones and tablets after acquired brain injury to support cognition
Bibliographic record
Abstract
OBJECTIVES: To describe the use of mobile devices after acquired brain injury (ABI), from the perspectives of injured individuals and significant others, and to examine factors associated with mobile device use for cognition. METHODS: Cross-sectional study with 50 adults with moderate/severe traumatic brain injury or stroke (42% women; mean of 50.7 years old, 4.6 years post-ABI), and 24 significant others. Participants completed questionnaires on mobile technology, cognitive functioning and the impact of technology. RESULTS: Of 45/50 adults with ABI who owned a smartphone/tablet, 31% reported difficulties in using their device post-injury, 44% had received support, and 46% were interested in further training. Significant others reported motor/visual impairments and the fear of becoming dependent on technology as barriers for mobile device use, and 65% mentioned that their injured relative needed additional support. Mobile device use for cognition was common (64%), predicted in a regression model by lower subjective memory and more positive perception of the psychosocial impacts of technology, and also associated in univariate analyses with younger age, lower executive functioning, and greater use of memory strategies. CONCLUSION: Using mobile devices for cognition is common post-ABI but remains challenging for a significant proportion. Developing training approaches may help supporting technology use.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".