Design and Application of a Game-Based WeChat Mini-Program for Screening Cognitive Function in Chinese Older Adults (Preprint)
Bibliographic record
Abstract
BACKGROUND Dementia seriously threatens the health and quality of life of older adults. Digital screening tools are more efficient than traditional paper-and-pencil cognitive assessments. OBJECTIVE To develop a novel game-based cognitive risk screening tool for middle-aged and older adults in China. METHODS Following the cognitive paradigm, Game-Based Cognitive Assessment – 3-Minute Version (G3) was designed as a WeChat mini-program. Pearson correlation tests were applied to the preliminary validation test of G3. RESULTS The G3 mini-program contains three 1-minute mini digital games—Number Ordering, Species Sorting, and Gold Finding—which support users’ self-assessment of cognitive functions and instant access to reports. G3 had a good correlation with the Montreal Cognitive Assessment Basic (r = .611, P < .001) among 60 older adults. CONCLUSIONS As a novel 3-minute game-based cognitive assessment tool, the G3 mini-program holds promises for cognitive disorder screening and home self-assessment.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".