Covid-19 pandemic-related changes in teleworking, emotional exhaustion, and occupational burnout: a cross-sectional analysis of a cohort study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic prompted significant shifts to teleworking, raising questions about potential impacts on employee wellbeing. This study examined the association between self-reported changes to teleworking frequency (relative to before the pandemic) and two indicators of occupational burnout: emotional exhaustion and professionally diagnosed burnout. METHODS: Data were derived from two samples from a digital cohort study based in Geneva, Switzerland: one population-based, and one from a sample of workers who were likely mobilized in the early stages of the COVID-19 pandemic. Emotional exhaustion was measured using the Maslach Burnout Inventory (EE-MBI), while self-reported diagnosed burnout was assessed by asking participants if they had received a professional diagnosis of occupational burnout within the previous 12 months. Participants were categorized based on self-reported telework frequency changes: "no change," "increase," "decrease," "never telework," and "not possible to telework." Adjusted regression models for each of the study samples were used to estimate associations between telework changes and burnout outcomes, accounting for sociodemographic, household, and work-related factors. RESULTS: In the population-based sample of salaried employees (n = 1,332), the median EE-MBI score was 14 (interquartile range: 6-24), and 7.3% reported diagnosed burnout. Compared to those reporting no change in telework frequency (19% of the sample), those reporting a decrease (4%) and those reporting that teleworking was not possible (28.7%) had significantly higher emotional exhaustion scores (adjusted beta (aβ) 5.26 [95% confidence interval: 1.47, 9.04] and aβ 3.51 [0.44, 6.59], respectively) and additionally reported higher odds of diagnosed burnout (adjusted odds ratio (aOR) 10.59 [3.24, 34.57] and aOR 3.42 [1.22, 9.65], respectively). "Increased" (28.9%) and "never" (19.4%) telework statuses were not significantly associated with burnout outcomes. These trends were mirrored in the "mobilized-workers" sample, with the exception that those reporting that teleworking was not possible did not report significantly higher odds of diagnosed burnout compared to those reporting no change in telework frequency. CONCLUSIONS: Decreased teleworking frequency and not having the possibility of telework were associated with higher emotional exhaustion and diagnosed burnout. As organizations reconsider their telework policies in a post-pandemic era, they should consider the impact of such organizational changes on employee wellbeing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".