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Record W3013279363 · doi:10.5430/ijhe.v9n3p202

Academic Performance, Employment, and Sleep Health: A Comparison between Working and Nonworking Students

2020· article· en· W3013279363 on OpenAlexvenueno aff
Yu Chih Chiang, Susan W. Arendt, Stephen G. Sápp

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsnot available
Fundersnot available
KeywordsFlexibility (engineering)Sleep (system call)PsychologyWork (physics)Working hoursTertiary sector of the economyService (business)Medical educationGerontologyApplied psychologyMedicineBusinessMarketingEngineeringLabour economicsManagementEconomics

Abstract

fetched live from OpenAlex

Interest in overall health and well-being of students in higher education has grown. Retention and success in college has been linked to various health aspects including sleep and alchol usage. The purpose of this study is to: 1) assess sleep health and related behaviors, 2) examine relationships between sleep health and work conditions, and 3) determine if there is a relationship between sleep health and academic performance. Because many students work in service industries due to the flexibility of these jobs, comparisons are made between students working in service industries, students working in other industries, and non-working students. The online survey data from 736 participants, representing six U.S. universities, was analyzed. Findings indicated that average grade point was associated with sleep, work hours, and household income; student employees working in the service industry had a sleep score slightly lower than students working elsewhere.

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 score0.253
Threshold uncertainty score0.418

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 integrity0.0000.001
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 head0.079
GPT teacher head0.427
Teacher spread0.348 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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