Telecommuting during COVID-19: How does it shape the future workplace and workforce?
Bibliographic record
Abstract
The objective of this research is to assess the impact of temporarily shifting the workforce to telecommuting on: (1) workplace policy changes, employee support, and future telecommuting plans, (2) employees' experience of telecommuting during COVID-19 and forecast of future telecommuting, and (3) differences among geographic areas, life circumstances, and demographic characteristics. The project employed a mixed-method approach, doing focus groups of human resources professionals in April 2021 and surveying workers and employers during the July through September 2021 period. Worker survey: Greater Minnesota respondents were more likely to telecommute no more than one day a week post-pandemic, while Twin Cities respondents were more likely to telecommute two to three days a week. Those with one or more children living at home were more likely to have a formal post-pandemic telecommuting agreement with their employers. Baby boomers were the most likely to telecommute four to five days a week post-pandemic. Gen Z respondents were the most likely to telecommute no more than one day a week post-pandemic. Employer survey: 71.4% of respondents indicated that most employees would return to in-person work post-pandemic, and 24.4% indicated the employers would only support infrequent (less than one day a month) telecommuting post-pandemic. Roughly a quarter indicated their organizations may recruit completely remote talent from outside of Minnesota. Employer representatives, compared to worker survey respondents, were much more likely to indicate their organizations had not developed a telecommuting policy for the future at the time of the survey. Worker survey respondents were much more likely to indicate that employers would support telecommuting anywhere between one and five days a week.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".