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Record W3159574360 · doi:10.1371/journal.pone.0250671

Considering cross-cultural differences in sleep duration between Japanese and Canadian university students

2021· article· en· W3159574360 on OpenAlexafffundabout
Benjamin Y. Cheung, Kosuke Takemura, Christine Ou, Anne Gale, Steven J. Heine

Bibliographic record

VenuePLoS ONE · 2021
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSleep (system call)ActigraphyAcculturationPsychologyEuropean Social SurveyDemographyGerontologyMedicineInsomniaEthnic groupPsychiatrySociologyPolitical science

Abstract

fetched live from OpenAlex

Sleep is a fundamental biological process that all humans exhibit, and there is much evidence that people suffer adverse health outcomes from insufficient sleep. Despite this evidence, much research demonstrates significant heterogeneity in the amounts that people sleep across cultures. This suggests that despite serving fundamental biological functions, sleep is also subject to cultural influence. Using self-report and actigraphy data we examined sleep among European Canadian, Asian Canadian, and Japanese university students. Significant cultural differences emerged in terms of various parameters of sleep (e.g. sleep time), and beliefs about sleep (e.g. perceived relation between sleep and health). Despite sleeping significantly less than European Canadians, Japanese participants slept less efficiently, yet reported being less tired and having better health. Moreover, relative to European Canadians, Japanese participants perceived a weaker relation between sleep and physical health, and had a significantly shorter ideal amount of sleep. Asian Canadians' sleep behaviors and attitudes were largely similar to European Canadians suggesting that people acculturate to local cultural sleep norms.

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 score0.076
Threshold uncertainty score0.974

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 head0.049
GPT teacher head0.283
Teacher spread0.234 · 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

Citations68
Published2021
Admission routes3
Has abstractyes

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