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Record W4212936858 · doi:10.7202/1086009ar

Telework in Canada : Who Is Working from Home during the COVID-19 Pandemic ?

2021· article· en· W4212936858 on OpenAlexaffvenueabout
James Chowhan, Kelly S. MacDonald, Sara L. Mann, Gordon B. Cooke

Bibliographic record

VenueRelations industrielles · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsMemorial University of NewfoundlandUniversity of GuelphYork University
Fundersnot available
KeywordsPandemicBachelorHuman capitalOddsWork (physics)Demographic economicsImmigrationMarital statusPsychologyCoronavirus disease 2019 (COVID-19)Survey data collectionDescriptive statisticsSample (material)SociologyDemographyPolitical scienceEconomic growthMedicineEconomicsPopulationEngineering

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has created a new reality in the world of work. Employers are realizing that to continue business operations during the pandemic they need to think differently about work : how it is organized, who does what and where the work is done. This paper addresses the question of whether there are differences in demographic and human capital characteristics between those who work from home during the pandemic and those who worked from home previously. Thus, this study takes advantage of the natural conditions of a pseudo experiment to identify the sociodemographic (i.e., sex (female/male), immigrant status, age) and human capital factors (i.e., education level, health) of those with access to telework to better understand the impact of the shutdown on these subgroups. This study uses Statistics Canada’s Canadian Perspectives Survey Series (CPSS) first survey data on the Impacts of COVID-19, and an analytic sample whose n = 2,653 ; further, the 2016 General Social Survey cycle 30 was used to provide pre-pandemic estimates for descriptive comparisons. We find that females are not less likely than males to participate in telework and that immigrant status is negatively related to work from home during the pandemic. Generally, there is support for an age relationship, with the odds of telework being relatively lower as age increases. Education level is positively associated with telework during the pandemic (e.g., having a bachelor’s or higher university degree is positively associated with telework). Finally, there is no relationship between physical or mental health and telework. This study contributes to the literature by quantifying the impact of a brief mass telework event and its implications for access to telework across sociodemographic and human capital characteristics. In a post-pandemic world, will we carry forward the lessons learned through this ‘experiment’ imposed by the pandemic ?AbstractThis study focuses on the demographic and human capital characteristics of Canadians that are associated with working from home (WFH), before and during the COVID-19 pandemic, or being absent from work, versus those Canadians who continue to work outside the home (i.e., who do not WFH). The results show significant differences in the incidence of WFH during the pandemic : 1) there are no significant differences between females and males ; 2) immigrants are less likely to WFH ; 3) younger workers are more likely to WFH ; 4) education is positively associated with WFH ; and 5) self-reported health is unrelated to WFH. The results from this natural experiment suggest potential policy and organizational implications if the pandemic WFH environment continues for an extended period of time.

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)0.003
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 score0.039
Threshold uncertainty score0.285

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0090.002
Scholarly communication0.0040.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.059
GPT teacher head0.285
Teacher spread0.226 · 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

Citations6
Published2021
Admission routes3
Has abstractyes

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