Assistive technologies designed to support executive function impairments while promoting independence: A scoping review
Bibliographic record
Abstract
Assistive technologies for cognition (ATC) can help alleviate some of the impacts of executive dysfunction and support independence. This article presents a scoping review to highlight the research gaps in this area. Search of scientific and gray literature was conducted in clinical and computer science databases, resulting in a selection of 27 papers. Traumatic brain injury and dementia were the disorders for which the most supports were available. Planning and carrying out tasks were the most supported executive function operations. Food preparation was the daily activity for which the most supports were developed. Diverse non-context-aware technologies were used to deliver primarily audio and visual prompts and cues. The performance of most of the technologies was tested among the target population to evaluate acceptability and effectiveness. This review showed that: (1) The goal formulation executive function operation needs to be the focus of more research; (2) the clinical context needs to be described in more be detail; (3) ATC development could benefit from the use of a wider range of user-centered methods, such as observational or ideation methods; (4) more evaluation of user outcomes is needed, such as impact on independence; and (5) a greater diversity of activities of daily living should be supported. Recommendations are presented.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".