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Record W2965449335 · doi:10.5539/gjhs.v11n10p16

The Effect of Mind Subtraction Meditation on Smartphone Addiction in School Children

2019· article· en· W2965449335 on OpenAlexvenueno aff
Yang Gyeong Yoo, Min Jeong Lee, Boas Yu, Mi Ra Yun

Bibliographic record

VenueGlobal Journal of Health Science · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsMeditationAddictionMental healthClinical psychologyAnxietyPsychologySmartphone addictionTest (biology)ImpulsivityMedicinePsychiatry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.347
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2019
Admission routes1
Has abstractyes

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