Working from home and role blurring: the effects of job pressure, organizational support, and caregiving responsibilities
Bibliographic record
Abstract
The performance of work-related tasks at home is associated with more frequent role blurring—but how do job pressure and organizational support for work-life balance modify that association? Using the job demands-resources model, we test these associations in a national sample of US workers. Drawing on data from the 2016 National Study of the Changing Workforce (NSCW), we observe that frequent performance of work at home is strongly associated with more role blurring, and this association is stronger among those with higher levels of job pressure and weaker among those with more organizational support for work-life balance. In addition, we ask whether the moderating effects further differ by caregiving responsibilities. We find that the moderating effect of job pressure on the association between working from home and role blurring is stronger for those with eldercare responsibilities but weaker for those with more children in the household. Overall, this study extends prior research by demonstrating how job demands and resources distinctly influence how working from home impacts role blurring. Moreover, the results underscore the importance of developing and implementing specific organizational policies that can effectively accommodate the diverse caregiving needs of those who work from home.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".