PENGARUH GAME BRAIN TRAINING TERHADAP PENINGKATAN FUNGSI KOGNITIF DI UKUR DENGAN MONTREAL COGNITIVE ASSESMENT VERSI INDONESIA (MOCA-INA) PADA MAHASISWA FAKULTAS KEDOKTERAN UNIVERSITAS MUHAMMADIYAH MALANG
Bibliographic record
Abstract
Background: Cognitive function of young adults (about the age of 20) mostly is not well developed, On that age, people needs better cognitive abilities to make adaptation as the new student of university. There are many ways to increase the cognitive function, one of them is brain training. Interestingly brain training can be done by game. The cognitive function can be measured by the more specific and sensitive tools i.e MoCA-Ina test. \nObjective: To determine the effect of brain training on improvement of cognitive function among medical students of Faculty of Medicine Universitas Muhamadiyah Malang. \nMethod: Experimental study with one group pre and post design. The subjects were medical students of Faculty of Medicine Universitas Muhamadiyah Malang that was applied by game brain training 30 minutes a day, 20 times in 4 weeks. Cognitive function was measured by MoCA-Ina test. Hypothesis tests was using Mc Nemar. \nResult: The percentage score of cognitive function before the intervention of NeuronationTM brain training was 24,97 and the percentage score after the intervention was 28,16. It shows improvement of cognitive function score after the intervention. McNemar test showed P of 0,000 (P<0,001), it means that game brain training increased cognitive function significantly. \nConclusion: The use of NeuronationTM brain training increased cognitive function significantly. \nKey words : Cognitive function, brain training, MoCA-Ina test, NeuronationTM.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".