Teleworking, Work Engagement and Intention to Quit During the COVID-19 Pandemic: A Study Examining the Effects of Individual and Organizational Characteristics
Bibliographic record
Abstract
Introduction: In 2020, the COVID-19 pandemic had an important effect on healthcare systems, including their healthcare workers (HCW).Studies on HCW well-being and mental health have regularly reported problems associated with their occupational activities during epidemics.The aim of this study is to describe the mental health impact and psychosocial perception of hospital workers one year after the first peak of the COVID-19 outbreak in France.Methods: The validated SATIN questionnaire was used to collect data on health and psychosocial factors.It was sent and selfadministered online in July 2021.In a multinomial regression model we included covariates: HCW status, age, gender, frontline worker, SARS-CoV-2 status.Results: Data from a total of 830 participants were included (64% were HCW).We found that worries about infection for oneself is a risk factor for negative perception of global health (OR 1,5 95% CI [1,029-2,199]), work demand (OR 1,8 [1,2-2,5]), work environment (OR 1,8 [1,3-2,5]), organizational context (OR 1,9 [1,1-3,3]), for psychosomatic symptoms (OR 2,1 CI [1,(1)(2)(3)9]) and stress (OR 1,8 [2,3]).Conclusion: We have shown that uncertainty about SARS-CoV-2 infection has an high mental health impact in hospital' workers.Actions on information, training, organizational context and appropriate protective equipment are useful and needed.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".