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Record W2608617504 · doi:10.1093/sleepj/zsx050.956

0957 SLEEP PATTERNS OF STUDENTS IN A SPORT STUDIES PROGRAM

2017· article· en· W2608617504 on OpenAlexaff
J. R. Roy, Frédérick Michaud, Isabelle Green‐Demers, Geneviève Forest

Bibliographic record

VenueSLEEP · 2017
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversité du Québec en Outaouais
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsBedtimePsychologySleep (system call)Excessive daytime sleepinessMedicineClinical psychologyPsychiatrySleep disorderInsomnia

Abstract

fetched live from OpenAlex

Teenagers go through modifications characterized by a delay in sleep-wake pattern. This contrasts with fixed schedules imposed by school demands and therefore may influence daytime functioning. However, specialized school programs, such as Sport Studies, may be more demanding because of additional training hours. The purpose of this study was to investigate sleep patterns of high school Sport Studies students and to verify its association with daytime sleepiness. Forty-eight Sport Studies students (15–17 years old) and 411 standard High School students (15–17 years old) completed questionnaires on sleep habits, sleep disorders and daytime functioning at the start of the school year (October). T-tests comparing both groups during school nights (SN) and weekend nights (WN) were performed on total sleep time (TST), bedtime and wake-up time. Then, t-tests were performed to compare both groups for the differences in sleep midpoint with SN and WN (∆sleep midpoint), sleep disorders (SD) and daytime sleepiness (DS). During SN, Sport Studies students have earlier bedtimes [Sport Studies=10:07PM±45min; High School=10:23PM±53min; t(457)=3.9, p<0.01] and wake-up times [Sport Studies=6:26AM±28min; High School=6:49AM±40min; t(457)=2.0, p<0.05], but no difference in TST [Sport Studies=7:52 ± 62min; High School=7:59 ± 58min; t(448)=0.7, p=0.47]. During WN, similar results were obtained with Sport Studies students having earlier bedtimes [Sport Studies =11:29PM±65min; High School=00:18AM±86min; t(440)=3.8, p<0.01] and wake-up times [Sport Studies=8:56AM±91min; High School=9:54AM±103min; t(455)=3.7, p<0.01] and no differences in TST [Sport Studies=9:03 ± 77min; High School=9:16 ± 91min; t(432)=0.9, p=0.36]. Finally, results showed a significant difference in ∆sleep midpoint [t(438)=3.2, p=0.001), less SD (t(377)=2.6, p=0.01] for the Sport Studies students, but no difference between groups in DS [t(444)=1.8, p=0.069]. This study suggests that Sport Studies programs are associated with different sleep habits in teenagers who seem to be shifting their sleep-wake patterns towards an earlier time compared to “regular” students. This earlier shifting is not associated with DS. However, this study did not investigate these sleep patterns and their impact over the course of an entire school year. Moreover, further studies should look more closely at different Sport Studies programs, since great variability exists among them, with programs like swimming having training sessions as early as 5:00AM. N/A

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.030
GPT teacher head0.392
Teacher spread0.362 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2017
Admission routes1
Has abstractyes

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