Bibliographic record
Abstract
Gamification and serious games are becoming increasingly important for training, wellness, and other applications. How can games be developed for non-traditional gaming populations such as the elderly, and how can gaming be applied in non-traditional areas such as cognitive assessment? The application that we were interested in is detection of cognitive impairment in the elderly. Example use cases where gamified cognitive assessment might be useful are: prediction of delirium onset risk in emergency departments and postoperative hospital wards; evaluation of recovery from stroke in neuro-rehabilitation; monitoring of transitions from mild cognitive impairment to dementia in long-term care. With the rapid increase in cognitive disorders in many countries, inexpensive methods of measuring cognitive status on an ongoing basis, and to large numbers of people, are needed. In order to address this challenge we have developed a novel game-based method of cognitive assessment. In this paper, we present findings from a usability study conducted on the game that we developed for measuring changes in cognitive status. We report on the game's ability to predict cognitive status under varying game parameters, and we introduce a method to calibrate the game that takes into account differences in speed and accuracy, and in motor coordination. Recommendations concerning the development of serious games for cognitive assessment are made, and detailed recommendations concerning future development of the whack-a-mole game are also provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".