Exploring the Impacts of the <scp>COVID</scp>‐19 Pandemic on Productivity: A Study of Accounting Faculty Who Are Caregivers of Children*
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".