Virtual Reality Simulation for Naming Tasks in the Montreal Cognitive Assessment (MoCA)
Bibliographic record
Abstract
Denne forskningen utforsker bruken av Virtual Reality (VR) som et verktøy for Montreal Cognitive Assessment (MoCA) navngivningsoppgaven, spesielt for personer med mild kognitiv svikt (MCI) og demens. Studien sammenligner den tradisjonelle papirbaserte metoden med to VR-versjoner – den ene bruker en kontroller og den andre bruker håndsporing. Resultatene viser at VR-versjonen med kontroller var mer nøyaktig enn både papirversjonen og VR med håndsporing. Når det gjelder tid, ble den papirbaserte metoden observert å være den raskeste, selv om det ikke var noen signifikant forskjell i tid mellom papirversjonen og VR med kontroller. VR med håndsporing tok imidlertid betydelig mer tid på grunn av sensorproblemer. Til tross for dette ga VR med en kontroller en mer engasjerende og oppslukende opplevelse for brukere, noe som tyder på at VR kan være et verdifullt verktøy i kognitive vurderinger. Studien identifiserte også områder for forbedring, spesielt innen håndsporingsteknologi. Funnene indikerer at VR har potensial i kognitive vurderinger, men videre forskning med større og mer mangfoldige deltakergrupper anbefales for å validere disse resultatene og forbedre teknologien.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".