Designing Feasible and Effective Cognitive Assessment for Older Adults in Long-Term Care
Bibliographic record
Abstract
Cognitive assessment and training are needed to avoid accelerated cognitive decline. We have developed BrainTagger, a suite of serious games that evaluate and potentially train cognitive abilities such as inhibitory control, processing speed, cognitive flexibility, and working memory. Ideally, cognitive assessments can be used as sentinels to detect problems that might damage brain functions (e.g., dehydration, poor nutrition, depression, delirium, inappropriate medication). In this paper, we report on a multi-year effort to develop hardware and software solutions that support effective implementation of cognitive assessment games (CAGs) in long-term care. Issues and constraints addressed include accommodating physical disabilities; making cognitive assessment enjoyable and engaging; providing scientific evidence that each game is measuring the intended construct; and reporting game results in a meaningful way. This article describes how we addressed some of these issues and demonstrates the sustained effort required to make this type of functionality work in practice. We also include some preliminary design guidelines, based on our experience, that may be useful in guiding future work on developing CAGs.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".