Job Satisfaction of Nurses in the Context of Clinical Supervision: A Systematic Review.
Ali HudaysFaye GaryJoachim G VossAhmed HazaziAmal ArishiFatimah Al-SakranPublished in: International journal of environmental research and public health (2023)
The purpose of this systematic review is to gather and analyze data from existing research on the effects of clinical supervision (CS) intervention on nurses' job satisfaction and related outcomes such as stress levels, burnout, and care quality. Using the PRISMA (preferred reporting items for systematic reviews and meta-analysis) criteria, a systematic review of the research available in the databases PubMed, PsycInfo, Cochrane Library, and CINAHL, well as Google Scholar, between January 2010 and May 2023 was carried out. Out of the 760 studies assessed, only 8 met the criteria for inclusion in the review based on Hawker's assessment tool. The results indicate that CS has a positive impact on nurses' job satisfaction and related outcomes such as reduced burnout, stress levels, and the quality of care. The study also found that the effectiveness of CS in enhancing job satisfaction was most evident during the 6-month follow-up period. However, nurses who did not receive CS did not show any noticeable improvement in their knowledge or practice. Additionally, nurses who required more efficient clinical oversight reported little to no positive impact on their practice or training. The review also highlighted gaps in knowledge regarding the frequency and number of sessions required for the impact of CS on nurses' job satisfaction and other outcomes. Due to the limited number of studies included in this review, further research is recommended to evaluate the influence of CS on nurses' job satisfaction.
Keyphrases
- healthcare
- systematic review
- mental health
- social support
- quality improvement
- randomized controlled trial
- meta analyses
- palliative care
- primary care
- depressive symptoms
- affordable care act
- big data
- adipose tissue
- metabolic syndrome
- insulin resistance
- stress induced
- case control
- data analysis
- deep learning
- adverse drug