Joint analyses of open comments and quantitative data: Added value in a job satisfaction survey of hospital professionals

Abstract

Objective: To obtain a comprehensive understanding of the job opinions of hospital professionals by conducting qualitative analyses of the open comments included in a job satisfaction survey and combining these results with the quantitative results.

Design: A cross-sectional survey targeting all Lausanne University Hospital professionals was performed in the fall of 2013.

Material and methods: The survey considered ten job satisfaction dimensions (e.g. self-fulfilment, workload, management, work-related burnout, organisational commitment, intent to stay) and included an open comment section. Computer-assisted qualitative analyses were conducted on these comments. Satisfaction rates on the included dimensions and professional groups were entered as predictive variables in the qualitative analyses.

Participants: Of 10 838 hospital professionals, 4978 participated in the survey and 1067 provided open comments. Data from 1045 respondents with usable comments constituted the analytic sample (133 physicians, 393 nurses, 135 laboratory technicians, 247 administrative staff, including researchers, 67 logistic staff, 44 psycho-social workers, and 26 unspecified).

Results: Almost a third of the comments addressed scheduling issues, mostly related to problems and exhaustion linked to shifts, work-life balance, and difficulties with colleagues' absences and the consequences for quality of care and patient safety. The other two-thirds related to classic themes included in job satisfaction surveys. Although some comments were provided equally by all professional groups, others were group specific: work and hierarchy pressures for physicians, healthcare quality and patient safety for nurses, skill recognition for administrative staff. Overall, respondents' comments were consistent with their job satisfaction ratings.

Conclusion: Open comment analysis provides a comprehensive understanding of hospital professionals' job experiences, allowing better consideration of quality initiatives that match the needs of professionals with reality.

Fig 1. Dendrogram representing the main classes and subclasses resulting from the IRaMuTeQ descending hierarchical classification analysis of the corpus.

Main class and subclass labels show the percentage of classified text segments.