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Self-Reported Academic Performance and Lifestyle Habits of School Children in Japan

2017· article· en· W2754102019 on OpenAlexvenueno aff
Jun Kohyama

Bibliographic record

VenueInternational Journal of Child Health and Nutrition · 2017
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGerontologyEnvironmental health

Abstract

fetched live from OpenAlex

Background and Objective: The purpose of this study was to determine the lifestyle habits significantly associated with self-reported academic performance (AP) in children in grades 5 to 12 in Japan. Methods: A total of 2,114 completed questionnaires were analyzed. Factors examined included habits related to sleeping, eating, defecation, physical activity, and screen time, in addition to body mass index (BMI). Social jet lag (SJL) was calculated from sleeping factors and categorized into five groups according to its value: minus 1 or less (SJL 1), more than minus 1 and 0 or less (SJL 2), more than 0 and 1 or less (SJL 3), more than 1 and 2 or less (SJL 4), and more than 2 (SJL 5). The association between self-reported AP and other factors except for SJL was assessed by means of multinomial logistic regression analysis. Results: Factors significantly associated with good self-reported AP included female gender, lower grade, less sleepiness, lower BMI, intake of breakfast, less constipation, early wake-up time during the weekend, and short screen time during the weekend. The mean self-reported AP of SJL 3 was better than that of both SJL 5 and SJL 1. Conclusions: Self-reported AP was associated with gender, grade, BMI, sleep, breakfast, defecation, and screen time in children in grades 5 to 12 in Japan. It must be ensured that children take enough time to perform the indispensable human behaviors of sleeping, eating, defecation, and physical activity.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.021
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.334
Teacher spread0.320 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

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