The Effect of Mind Subtraction Meditation on Smartphone Addiction in School Children
Bibliographic record
Abstract
BACKGROUND: Aim of current study was to examine the effects of school-based mind subtraction meditation program on smartphone addiction tendency and mental health of third grade students in a South Korean elementary school. MATERIALS & METHODS: A quasi-experimental design with nonequivalent comparison groups was used. An experimental group (n = 24), who participated in the school-based meditation program, and a control group (n = 22), who did not participate in the program, were measured pre-test, post-test, and also three months after the completion of this study on smartphone addiction tendency and mental health. RESULTS: The study result showed a significant decrease in smartphone addiction tendency and also significant improvements in mental health variables of depression, anxiety, aggression, and impulsivity for the experimental group. These improvements were shown to continue even after the study completion when measured post-intervention at three months. CONCLUSION: This study demonstrated that the mind subtraction meditation program had positive effects on smartphone addiction tendency and mental health variables. It can be suggested from this study to recommend mind subtraction meditation as one of feasible strategies to prevent smartphone addiction and to improve mental health status in elementary school children. Further, this study meaningfully supports positive beneficial evidence of meditation program utilization in schools.
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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".