Predicting Burnout in Mental Health Staff v2.1
Research type
Research Study
Full title
Development of a Prognostic Model of Mental Health Staff Burnout Using Machine Learning
IRAS ID
256342
Contact name
Benjamin Davis
Contact email
Sponsor organisation
University of Sheffield
Duration of Study in the UK
0 years, 6 months, 13 days
Research summary
Burnout amongst NHS staff working clinically in mental health services is an acknowledged problem with reported levels of burnout as high as 67%. Burnout consists of two key components of emotional exhaustion as well as cynical attitudes towards employers and service users. Burnout levels have been shown to relate to increased staff turnover and, more recently, to poorer clinical outcomes. A range of factors have been shown to relate to higher levels of staff burnout and these can be categorised as organisational and personal factors. These factors include workload, supervision, organisational climate, conflict with family responsibilities, job autonomy and belief in one's abilities. Whilst a large number of studies have evaluated a range of these factors, these have mostly only measured burnout at a single time-point. As such, causal relationships cannot be inferred and so understanding which factors might be predictive of burnout has so far been lacking.
This study aims to collect information on a comprehensive set of factors that have been previously shown to relate to burnout levels and subsequently collect monthly burnout and job satisfaction information over a period of 6 months. Advanced data analysis will then enable the development of a predictive model of burnout in mental health staff using the most important set of factors from the analysis. It is intended that this information can be used to inform on organisational and individual interventions to support staff going forward.
The study will last approximately 7 months.
REC name
East of England - Cambridge Central Research Ethics Committee
REC reference
19/EE/0054
Date of REC Opinion
14 Feb 2019
REC opinion
Favourable Opinion