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Record W4389294483

The Relationship of Sleep Duration with Ethnicity and Chronic Disease in a Canadian General Population Cohort

2020· article· en· W4389294483 on OpenAlexaboutno aff
Mandeep Singh, Hall KA, Amy C. Reynolds, Palmer Lj, Sutapa Mukherjee

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2020
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupCohortMedicineDuration (music)DiseaseSleep (system call)PopulationDemographyCohort studyGerontologyPediatricsInternal medicineEnvironmental healthPolitical scienceSociology
DOInot available

Abstract

fetched live from OpenAlex

Mandeep Singh,1– 3 Kelly A Hall,4 Amy Reynolds,5 Lyle J Palmer,4,* Sutapa Mukherjee6,7,* 1Department of Anesthesiology and Pain Management, Women’s College Hospital, Toronto, Canada; 2Department of Anaesthesiology and Pain Management, Toronto Western Hospital, University Health Network, Toronto, Canada; 3Toronto Sleep and Pulmonary Centre, Toronto, Canada; 4School of Public Health, University of Adelaide, Adelaide, SA, Australia; 5Appleton Institute, CQ University Australia, Wayville, SA, Australia; 6Respiratory and Sleep Services, Southern Adelaide Local Health Network, Adelaide, SA, Australia; 7Adelaide Institute for Sleep Health, Flinders University, Adelaide, SA, Australia*These authors contributed equally to this workCorrespondence: Sutapa MukherjeeAdelaide Institute for Sleep Health, Flinders University, Adelaide, SA, AustraliaTel +61 8 8201 7925Email Sutapa.Mukherjee@sa.gov.auStudy Objectives: Sleep duration is an important marker of sleep quality and overall sleep health. Both too little and too much sleep are associated with poorer health outcomes. We hypothesized that ethnicity-specific differences in sleep duration exist.Methods: This cross-sectional study utilized questionnaire data from the Ontario Health Study (OHS), a multi-ethnic population-based cohort of Canadian adult residents aged 18 to 99 years, who provided medical, socio-demographic, and sleep information. Generalised linear models were used to investigate the association of sleep duration with ethnicity.Results: The study sample consisted of 143,307 adults (60.4% women). The sample was multi-ethnic, including self-identified Aboriginal, Arab, Black, Chinese, Filipino, Hispanic, Japanese, Korean, Mixed (> 1 ethnicity), South Asian, South-East Asian, West Asian, and White ethnicities. Univariate analyses found that mean sleep duration compared to the White reference group (7.34 hours) was shorter in the Filipino (6.93 hours, 25 min less), Black (6.96 hours, 23 min less), Japanese (7.02 hours, 19 min less), Chinese (7.23 hours, 7 min less), and Mixed (7.27 hours, 4 min less) groups (all P< 0.001). Mean sleep duration was shorter in men (7.25 hours) compared to women (7.37 hours) in the cohort as a whole (P< 0.001), and in all ethnic groups (P< 0.001). Multivariate analyses, adjusted for a wide range of potential risk factors, and analysis of sleep duration as a categorical variable (“short”, “average”, and “long” sleepers) confirmed these relationships. Both sleep duration and ethnicity were independent significant predictors of a range of physician-diagnosed morbidities including diabetes, stroke, and depression.Conclusion: Important differences exist in sleep duration between ethnic groups and may contribute to observed health disparities. Our results highlight the need for ethnicity-specific targeted education on the importance of prioritizing sleep for good health, and the need to account appropriately for ethnicity in future epidemiological, clinical, and translational research into sleep and related conditions.Keywords: sleep health, sleep duration, ethnicity, population health, health disparity

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)0.001
metaresearch head score (Gemma)0.002
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 score0.026
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.143
GPT teacher head0.491
Teacher spread0.347 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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