sFEra APP: description and usability of a novel tablet application for executive functions training
Bibliographic record
Abstract
Executive functions include functions such as planning, working memory, inhibition, mental flexibility, action monitoring and initiation, and are essential to carry out an independent everyday life. Individuals suffering from brain injury, such as a stroke, very commonly experience executive deficits that reduce the capacity to regain functional independence. In recent years, there has been a growing interest in developing tablet computer-based cognitive training programs for stroke patients. In this respect, we described and evaluated the usability of a novel tablet application (app) for executive function training, developed in the context of the MEMORI-net project, a cross-border Italy-Slovenia program for the rehabilitation of stroke patients. We conducted a pilot study with a non-clinical sample of 16 participants to obtain information about the usability of the app. Our descriptive analyses suggest that most users were satisfied with the overall experience and the app was highly usable, even with little previous experience with tablet applications. Acceptability and effectiveness will need to be evaluated in a clinical randomized controlled study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.015 | 0.003 |
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".