Influence of nursing staff working hours on stress levels during the COVID-19 pandemic

  1. Manuela Hoedl
  2. Silvia Bauer
  3. Doris Eglseer

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Abstract

Background

Working as a nurse means being able to provide high-quality care 24/7. Studies have shown that the average number of working hours per week is a significant predictor of stress and that the severity of the coronavirus disease 2019 (COVID-19) pandemic has increased the nurses’ stress levels.

Objective

The aim of this study was to investigate the influence of the nursing staff’s working hours during the COVID-19 pandemic on the perceived level of stress.

Method

We carried out an online cross-sectional survey and measured the stress level with the perceived stress scale.

Results

Most of the nurses experienced a moderate level of stress. We identified a statistically significant association between increased numbers of working hours per week and the nurses’ perceived stress level. In addition, 15% of the nurses who had worked more than 40 h reported experiencing a high level of stress.

Conclusion

These results reflect the negative consequences of prolonged working hours. For this reason, a (inter)national discussion is needed on the topic of restricting the working hours of healthcare workers during such pandemics. This discussion can improve the health and safety of healthcare workers, patients and members of the general population.

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  1. Version published to 10.1007/s16024-021-00354-y
  2. ScreenIT

    SciScore for 10.1101/2020.08.12.20173385: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIACUC: Ethics: The ethical committee of the Medical University of Graz approved the study (32-386 ex 19/20).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.08.12.20173385v1 on medRxiv