Managing Virtual Presenteeism during the COVID-19 Pandemic: A Multilevel Study on Managers’ Stress Management Competencies to Foster Functional Presenteeism
Bibliographic record
Abstract
Teleworking remains an attractive option for many workers since the COVID-19 pandemic, but it presents significant management challenges, particularly when employees face health issues. The management of virtual presenteeism, where employees continue teleworking despite being ill, has received limited attention. This study explores the relationship between managers' stress management competencies (SMCs), mental health, and job performance of virtual presentees, aiming to fostering more functional presenteeism. We examine whether managers' SMCs promote functional presenteeism by comparing managers' self-assessments with employee assessments, and analyzing how agreement levels between the two affect mental health and job performance. Data were collected from 365 teleworkers supervised by 157 managers in a large public organization in Québec. The results indicate that virtual presentees' mental health and job performance are closely linked to employees' assessment of their managers' SMCs. Employees who agreed with their manager or overestimated their managers' SMCs exhibited better mental health and job performance than those who agreed with their manager on low SMCs or underestimated their managers. This study expands on the health-performance framework of presenteeism and self-other agreements, highlighting management practices that should be enhanced in the context of virtual presenteeism.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".