Mobile app learning in memory intervention for acquired brain injury: Neuropsychological associations of training duration
Bibliographic record
Abstract
Memory impairment is a common consequence of acquired brain injury, often leading to functional difficulties day-to-day and decreased independence. Memory Link is a theory-driven training programme for individuals with moderate-to-severe memory dysfunction, which enables the acquisition of digital device skills for functional compensation. The present study examined how neuropsychological functioning and initial training performance contribute to training duration in our outpatient memory rehabilitation programme. A retrospective chart review was conducted, extending 12 years into the past, yielding data from 37 eligible participants. All participants demonstrated skill learning of the calendar function in their digital device to the criterion point. The results showed that performance on neuropsychological tests of explicit memory (e.g., CVLT-II, BVMT-R), processing speed (e.g., Digit Symbol Coding, Trail Making sequencing), executive functioning (e.g., Trail Making switching), and perceptual ability (i.e., Block Design) were significantly associated with training duration to learn the core steps of calendar use. Furthermore, linear regression revealed that initial training performance was a significant predictor of training duration. Lastly, profile of cognitive impairment, with regard to severity of memory functioning and the presence of additional deficits, was found to be a significant factor contributing to how many training trials were required to learn application skills.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".