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Record W3133840223 · doi:10.1111/aphw.12261

Stress, physical activity, and screen‐related sedentary behaviour within the first month of the COVID‐19 pandemic

2021· article· en· W3133840223 on OpenAlexaff
Sarah J. Woodruff, Paige Coyne, Emily St‐Pierre

Bibliographic record

VenueApplied Psychology Health and Well-Being · 2021
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPandemicPhysical activityCoronavirus disease 2019 (COVID-19)Analysis of varianceScreen timePsychologySedentary lifestyleBivariate analysisDemographyStress (linguistics)MedicineGerontologyPhysical therapyDiseaseInternal medicineSociologyStatistics

Abstract

fetched live from OpenAlex

This study investigated how stress, physical activity and sedentary behaviours, of a small sample of Canadians, changed within the first month (i.e. March/April) of the COVID-19 pandemic and the reasons/barriers associated with such changes. Individuals who regularly wear activity trackers were recruited via social media. Participants (N = 121) completed fillable calendars (March/April 2020) with their step counts and answered an online survey. Separate paired-sample t-tests, one-way ANOVAs and bivariate chi-squares were conducted, in addition to qualitative analysis. Daily (p <.001) and work (p =.003) stress increased, physical activity (measured by step count) decreased (p =.0014), and screen-related sedentary behaviour increased (p <.001) as a result of COVID-19. A decrease in physical activity, as a result of the pandemic, was also associated with a larger increase in work stress, compared with those who self-reported their physical activity to have been maintained or increased (p =.005). The most common reasons/barriers to changes in physical activity behaviours were access/equipment, time and motivation. Findings provide initial evidence of the impact of the COVID-19 pandemic on the health of some Canadians and highlight the need for continued monitoring of the health of Canadians throughout the pandemic.

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)0.000
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 score0.041
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.030
GPT teacher head0.352
Teacher spread0.322 · 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

Citations44
Published2021
Admission routes1
Has abstractyes

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