Literature DB >> 25030488

Problems of clinical nurse performance appraisal system: a qualitative study.

Nasrin Nikpeyma1, Zhila Abed Saeedi2, Eznollah Azargashb3, Hamid Alavi Majd4.   

Abstract

PURPOSE: The purpose of this study is to explore problems of clinical nurse performance appraisal system.
METHODS: This study employed a descriptive qualitative approach. The participants were purposively selected from clinical nurses working across all of the hospital units in a large metropolitan teaching hospital in Tehran, Iran in 2012. Data were collected using five focus group interviews, which were audio taped. The number of participants in each group ranged from 7 to 10. The semi-structured interviews were guided by a set of nondirective questions, and continued until the data reached saturation. Data were analyzed using framework analysis.
RESULTS: Four major themes regarding the problems of clinical nurse performance appraisal system emerged from the analysis of textual data. These themes were contextual problems, problems related to performance appraisal structure, problems related to performance appraisal process and those related to performance appraisal results.
CONCLUSION: The findings of this study reveal that the nurse performance appraisal system confronts with various problems. Some of these problems are related to organizational context while the others concerned structure, process and results of the performance appraisal system. In order to achieve high quality of patient care as the final goal of performance appraisal, changing and revision of this system is necessary.
Copyright © 2013. Published by Elsevier B.V.

Entities:  

Keywords:  employee performance appraisal; nurses; qualitative research

Mesh:

Year:  2013        PMID: 25030488     DOI: 10.1016/j.anr.2013.11.003

Source DB:  PubMed          Journal:  Asian Nurs Res (Korean Soc Nurs Sci)        ISSN: 1976-1317            Impact factor:   2.085


  3 in total

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3.  Construction of the Public Management Performance Assessment Algorithm Using Fuzzy Clustering.

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