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Record W4290729589 · doi:10.1097/jom.0000000000002663

Working From Home During the COVID-19 Pandemic

2022· article· en· W4290729589 on OpenAlexafffund
Wei Zhang, Huiying Sun, Aaron Gelfand, Richard Sawatzky, Alison Pearce, Aslam H. Anis, Katrina Prescott, Christine M. Lee

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of British ColumbiaWestern University
FundersCanadian Institutes of Health Research
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)BetacoronavirusVirologyCoronavirus InfectionsMedicineOutbreakInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to measure the association of working from home (WFH) with work productivity loss due to caregiving responsibilities or health problems during the COVID-19 pandemic. METHODS: We conducted an online survey of family/friend caregivers (n = 150 WFH/75 non-WFH) and patients (n = 95/91) who worked during the past 7 days in May and July 2020, respectively. Absenteeism and presenteeism were measured using the Valuation of Lost Productivity questionnaire. RESULTS: Working from home was associated with higher odds of absenteeism (odds ratio, 2.53; 95% confidence interval, 1.11 to 5.77) and presenteeism (2.79; 1.26 to 6.18) among caregivers and higher odds of presenteeism among patients (2.78; 1.13 to 6.84). However, among caregivers with absenteeism more than 0 days, WFH was significantly associated with fewer absent workdays. CONCLUSIONS: Working from home was not associated with overall absenteeism and presenteeism in caregivers or patients. Working from home allows a more flexible and inclusive workplace without impacting productivity, although further research is needed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.066
GPT teacher head0.319
Teacher spread0.253 · 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.

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
Published2022
Admission routes2
Has abstractyes

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