The Effect of Brain Training Game activities on Improvement of Cognitive Function measured by Montreal Cognitive Assesment Indonesia version (MoCA-Ina)
Bibliographic record
Abstract
The function of cognition in young adults (around the age of 20 years) mostly does not develop to its peak, even though at that age a person needs better cognitive abilities to deal with the lectures. Brain training game activities by playing games through the NeuronationTM application can improve cognitive function. To determine the effect of Brain training activities on improvement of cognitive function. Experimental study with two group pre and post design. 74 Sample were medical students of Faculty of Medicine, University of Muhammadiyah Malang class of 2017-2018, divided into control and treatment groups of 37 samples each. The treatment group was given by game brain game training 30 minutes a day, 20 times in 4 weeks.. Cognitive function was measured by the MoCA-Ina test in the control group and pre and post test. The hypothesis test used the pairet samples test. Cognitive function of pre and post test of the control group good cognitive function increased 2.69% and cognitive function of pre and post test of treatment group increased 58.8% with the result of pairet sample analysis of significant p = 0,000 which means that in the control group there was a tendency for increased function cognition after treatment (post test) but the improvement was not significant. In the treatment group with brain training activities cognitive function increased sharply and statistically significant, Brain training activities affect the improvement of cognitive function.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".