Work from Home and Perceptions of Career Prospects of Employees with Children
Bibliographic record
Abstract
this study explores how various work and family-related contexts moderated the link between work-from-home (WFH) and self-perceived changes to the career prospects among employees with children after over a year of the COVID-19 pandemic. We argue that the link between WFH and the perception of changes to one’s career prospects is likely to differ depending on gender, occupation, whether the employee has worked from home before the pandemic, how much time their children spent at home due to pandemic restrictions and the cohabiting status of the parent. We conducted fixed effects multinomial regression models using a unique multicountry dataset, including representative samples of parents with dependent children from Canada, Germany, Italy, Poland, Sweden, and the US. Employees with children who had prior experience with WFH before the pandemic were more likely to report improved career prospects than those who worked solely in the office. The positive effect of WFH for newcomers to the world of remote work was less unequivocal and varied based on occupation and gender. We also find that the presence of children at home and the cohabitation status substantially moderate the link between WFH and perceived changes to one’s career prospects, with different implications based on the employee's gender. We fill the research gap by showing how fluid workers' perceptions of career prospects depend on varying professional (prior experience with WFH and occupation) and personal (increased family demands) situations. This study also indicates the need for context-sensitive career management in organisations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".