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Record W4404646097 · doi:10.5206/eei.v34i2.16687

Sleep and Well-Being During the COVID-19 Pandemic: Remote and In-Person Learning for College Students With and Without Disabilities

2024· article· en· W4404646097 on OpenAlexaffvenue
Catherine S. Fichten, Georgiana Alexandra Costin, Mary Jorgensen, Alice Havel, Susie Wileman, Samantha Wing, Sally Bailes, Laura Creti, Eva Libman

Bibliographic record

VenueExceptionality Education International · 2024
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsMcGill UniversityDawson CollegeJewish General Hospital
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicPsychology2019-20 coronavirus outbreakLearning disabilitySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Mathematics educationPedagogyDevelopmental psychologyMedicineVirology

Abstract

fetched live from OpenAlex

In an email-based questionnaire study, we investigated chronotype, sleep, and well-being among junior/community college students with (n = 52) and without disabilities (n = 27) during remote (COVID-19–related) and subsequent in-person learning periods. Overall, we found no significant differences between students with and without disabilities either in chronotype or in sleep quality. Morningness and intermediate chronotypes were related to better sleep quality during both the remote and in-person periods. We also found that sleep quality was better during the remote period than during the in-person period. This finding was robust as we identified this both in quantitative and qualitative results. We also discovered that, surprisingly, students had little concern with the possibility of catching the COVID-19 virus. Findings on well-being during the remote and in-person periods were mixed, although we noted mainly negative experiences during the in-person period. The findings make it clear that return to “in person” was not “return to normal.”

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)0.001
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 score0.021
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.058
GPT teacher head0.448
Teacher spread0.390 · 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
Published2024
Admission routes2
Has abstractyes

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