Early mobile app training proficiency predicts how well memory-impaired individuals learn to use digital memory aids in the real world
Bibliographic record
Abstract
Functional memory impairment following acquired brain injury can lead to decreased independence. External memory aids such as smartphones can be highly effective compensation tools, but cognitive deficits may create barriers to implementation in daily life. The present study examined predictors of real-world use of mobile calendar applications for memory compensation in an acquired brain injury sample. A retrospective chart review was completed from an outpatient rehabilitation program, extending 15 years into the past, yielding data from 34 eligible participants. All participants demonstrated skill learning of the calendar function in their digital device and subsequently completed the generalization phase of training, which is focused on real-world implementation (measured through prospective memory tasks). The results showed that the length of time required for skill learning of mobile calendars (event entry or responding to alerts) was not predictive of the duration of generalization training. Initial training performance for responding to alerts, but not event entry, was a significant predictor of the duration of generalization training needed to complete the program. A secondary analysis with a subset of the data revealed that individuals with additional executive deficits took significantly longer to complete generalization training compared to those with a more focal memory impairment.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".