Effects of a Mindfulness Meditation Course on Learning and Cognitive Performance among University Students in Taiwan
Bibliographic record
Abstract
Mindfulness training has recently gained much research interest because of its putative benefits for both mental and physical health. However, little is available in its effects on Asian students. Therefore, a quasi-experimental pre/posttest design was used to assess the effects of a one-semester mindfulness meditation course in 152 first-year Taiwanese university students and compared with 130 controls. The Chinese version of the College Learning Effectiveness Inventory (CLEI) and a computer software program focused on specific cognitive tasks were used for the evaluation. Results from the analysis of covariance revealed that while the score of the full CLEI scale was significantly higher in the intervention group compared with the control (P = 0.022), none of the comparisons between the nine CLEI subscales were significantly different between the two groups. For the computer cognitive tasks, the intervention group exhibited significantly better performance in the accuracy of the digital vigilance task (P = 0.048), choice reaction time (P = 0.004), spatial working memory (P = 0.042), and digital vigilance task reaction time (P = 0.004). This study showed that a one-semester mindfulness meditation course was able to improve learning effectiveness and both attention and memory aspects of cognitive performance among Taiwanese university students.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".