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Record W3169704379 · doi:10.52609/jmlph.v1i1.5

Sleep Disturbance During Quarantine in the Era of the SARS-CoV-2 (COVID-19) Pandemic

2021· article· en· W3169704379 on OpenAlexvenueno aff
Seham Sahli, Sharafaldeen Bin Nafisah

Bibliographic record

VenueThe Journal of Medicine Law & Public Health · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsPittsburgh Sleep Quality IndexQuarantineAnxietyCoronavirus disease 2019 (COVID-19)PandemicMedicineSleep (system call)ConfoundingSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Affect (linguistics)Sleep disorderSleep qualityInternal medicinePsychologyPsychiatryInsomniaDiseasePathology

Abstract

fetched live from OpenAlex

ABSTRACT Background Quarantine has been shown to affect sleep quality in previous analyses. However, a thorough investigation of the association between sleep disturbance and COVID-19 infection during quarantine is still lacking. Aim We aim to determine the impact of quarantine on sleep quality and such impact to anxiety. We also aim to investigate the use of medication and its impact on sleep quality during quarantine. Methods A cross-sectional study conducted in the Jazan region of Saudi Arabia during September 2020. The Pittsburgh Sleep Quality Index (PSQI) and the Generalised Anxiety Disorder Assessment (GAD-7) were used. Results The number of participants was 327, with an infection rate of 53.6% (n= 175). 60.8% (n=189) were quarantined. The mean PSQI score was 5.69 (SD=3.17), those who were quarantined had a higher score (M=6.33, SD=2.99) than those who were not (M=4.57, SD=3.23). After we control for the confounding of anxiety, the PSQI score was also higher in those quarantined (M=0.59, SD=0.26) than in those who were not (M=0.48, SD=0.31); t(120)=2.08, p<0.05. Zinc was noted to have a significant positive effect on sleep quality and anxiety level. Conclusion This analysis provides new insight into the effect of quarantine and anxiety levels on sleep quality.

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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.171
GPT teacher head0.457
Teacher spread0.286 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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