Too many roles, not enough time: a comparative case study of the impact of the COVID-19 pandemic on Canadian and Brazilian professors work roles
Bibliographic record
Abstract
Although role conflict has been widely studied, little is known about conflicts arising from managing multiple work roles, particularly how the COVID–19 pandemic and national context affected them. Our research seeks to answer the following research questions: (1) What impact did the pandemic have on faculty members' allocation of time across research, teaching and administrative roles? (2) What impact did the pandemic have on the extent to which professors experienced three forms of intra–role work conflict: research–teaching, research–administration, administration–teaching? and (3) What impact does national culture have on these dynamics? Using a qualitative case study methodology, we surveyed and interviewed 23 Canadian and 24 Brazilian business school professors. Our analysis shows that the pandemic intensified time demands and role conflict for faculty in both countries. While time in teaching increased dramatically for professors in both countries, we observed several between–country differences in work role conflict. National culture shaped these dynamics: Brazilian professors, reflecting their collectivist culture, spent more time nurturing student relationships, while Canadian faculty reported greater reductions in research time due to competing demands from teaching and family roles. The findings provide insights for universities seeking to develop culturally sensitive policies that support faculty and mitigate role conflicts.
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 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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.037 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".