Paradoxical Effects of Teleworking on Workers’ Well-Being in the COVID-19 Context: A Comparison Between Different Public Administrations and the Private Sector
Bibliographic record
Abstract
This study examines workers’ well-being during the first lockdown by comparing teleworkers to on-site workers across the private sector and public administrations. Using a sample of workers ( N = 471) collected online, we noted a positive association between telework and well-being. When sector is introduced, this relationship disappears, and public service workers display a higher level of well-being compared with health and social service workers. The impact of teleworking differs across sectors, highlighting the relevance of the contingent approach of human resource management (HRM). Nonetheless, our results indicated that teleworkers who prefer the segmentation of work–life boundaries display a lower level of well-being than those who prefer the integration of these boundaries. For HRM practitioners of all sectors, this finding is essential to remember after the pandemic because organizations should avoid imposing teleworking universally. Flexibility will be required to be inclusive and to preserve the well-being of all employees.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".