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Record W4381336639 · doi:10.1111/1911-3838.12341

Exploring the Impacts of the <scp>COVID</scp>‐19 Pandemic on Productivity: A Study of Accounting Faculty Who Are Caregivers of Children*

2023· article· en· W4381336639 on OpenAlexaffvenueabout
Sara Wick, Camillo Lento

Bibliographic record

VenueAccounting Perspectives · 2023
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsLakehead UniversityUniversity of Guelph
Fundersnot available
KeywordsProductivityPandemicWork (physics)Service (business)PsychologyCoronavirus disease 2019 (COVID-19)AccountingBusinessMedical educationMedicineMarketingEconomic growthEconomics

Abstract

fetched live from OpenAlex

ABSTRACT This study explores how the COVID‐19 pandemic affected the productivity of accounting faculty who identify as caregivers of children. We examine the effects on caregivers of children because of the significant shift in the family‐work interface that resulted from remote working and learning. We draw on existing family‐work conflict research to develop four hypotheses that explore why the productivity of accounting faculty who are caregivers of children might be affected differently during the pandemic than that of accounting faculty who are non‐caregivers of children. We surveyed accounting faculty primarily across Canada and the United States. We find that accounting faculty caring for children during the pandemic experienced reduced research, teaching, and service productivity because of increased family‐work conflict and depletion. We supplement our main findings with an analysis of open‐ended questions to further understand productivity changes and supports for research, teaching, and service. Our study contributes to research examining family‐work conflict, employee productivity, and the accounting profession by making practical recommendations for providing targeted support for caregivers of children during times of crisis.

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)0.004
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 score0.103
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.090
GPT teacher head0.338
Teacher spread0.248 · 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

Citations6
Published2023
Admission routes3
Has abstractyes

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