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Record W3125568351 · doi:10.3390/clockssleep3010007

Prevalence of Insomnia in Two Saskatchewan First Nation Communities

2021· article· en· W3125568351 on OpenAlexafffundabout
James A. Dosman, Chandima Karunanayake, Mark Fenton, Vivian R. Ramsden, Robert Skomro, Shelley Kirychuk, Donna Rennie, Jeremy Seeseequasis, Clifford Bird, Kathleen McMullin, Brooke Russell, Niels Koehncke, Thomas Smith-Windsor, Malcolm King, Sylvia Abonyi, Punam Pahwa

Bibliographic record

VenueClocks & Sleep · 2021
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsVictoria HospitalPrince Albert Grand CouncilUniversity of SaskatchewanRoyal University Hospital
FundersCanadian Institutes of Health Research
KeywordsInsomniaLogistic regressionDepression (economics)Medical prescriptionMedicineMental healthPsychiatry

Abstract

fetched live from OpenAlex

Insomnia is a common problem in Canada and has been associated with increased use of health care services and economic burden. This paper examines the prevalence and risk factors for insomnia in two Cree First Nation communities in Saskatchewan, Canada. Five hundred and eighty-eight adults participated in a baseline survey conducted as part of the First Nations Sleep Health Collaborative Project. The prevalence of insomnia was 19.2% among participants with an Insomnia Severity Index score of ≥15. Following the definition of nighttime insomnia symptoms, however, the prevalence of insomnia was much higher, at 32.6%. Multivariate logistic regression modeling revealed that age, physical health, depression diagnosis, chronic pain, prescription medication use for any health condition, and waking up during the night due to terrifying dreams, nightmares, or flashbacks related to traumatic events were risk factors for insomnia among participants from two Saskatchewan Cree First Nation communities.

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 score0.036
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.289
Teacher spread0.267 · 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

Citations8
Published2021
Admission routes3
Has abstractyes

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