Collegial surface acting emotional labour, burnout and intention to leave in novice and pre‐retirement nurses in the United Kingdom: A cross‐sectional study

Research output: Contribution to journalArticle

Abstract

Aim: To investigate the relationship between surface and deep acting in nurses' patient-focused and collegial emotional labour, with emotional exhaustion, depersonalization and personal accomplishment and intention to leave.
Design: A cross-sectional descriptive study using the Emotional Labour Scale, the Maslach Burnout Inventory and intention to leave Yes/No questions with 118
Registered Nurses to measure patient-focused and collegial emotional labour, burnout and intention to leave.
Results: Surface acting in patient-focused and collegial emotional labour was found to have positive associations with burnout and intention to leave their current job. Only surface acting in patient-focused emotional labour was positively associated with intention to leave the organization and/or the profession. The novice nurses carried out more deep acting collegial emotional labour than the pre-retirement nurses.
Conclusions: Collegial emotional labour is significant to nurses' intention to leave their current job but not their intention to leave the organization and/or the profession.
Original languageEnglish
Pages (from-to)463-472
Number of pages10
JournalNursing Open
Volume8
Issue number1
DOIs
Publication statusPublished - 15 Oct 2020

Bibliographical note

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited

Keywords

  • burnout
  • collegial emotional labour
  • cross-sectional study
  • intention to leave
  • nurses
  • surface acting

Fingerprint Dive into the research topics of 'Collegial surface acting emotional labour, burnout and intention to leave in novice and pre‐retirement nurses in the United Kingdom: A cross‐sectional study'. Together they form a unique fingerprint.

Cite this