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Cited by 200

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Nurse job satisfaction and retention: comparing public to private hospitals in Jordan

MRAYYAN, MAJD TAWFEEQ·2005·Journal of Nursing Management

Abstract

AIMS: To identify variables of Jordanian nurses' job satisfaction and retention. Comparisons were performed between three public and two private hospitals. BACKGROUND: There has been little research on nurse job satisfaction and retention in Jordan. Interest in nurse retention is renewed with each cycle of nursing shortage. METHODS: A descriptive design using surveys guided this study through convenience sample of 438 nurses. Data were analysed using descriptive and inferential statistics. RESULTS: Nurses reported that they were "moderately satisfied" in their jobs with "neutral" opinion about their retention. Nurses who work in private hospitals were more satisfied and intended to retain their jobs more than nurses in public hospitals. CONCLUSION: Nurse job satisfaction and retention are related concepts; nurses who are satisfied in their jobs are likely to retain these jobs.

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Metadata

Authors
MRAYYAN, M. T.
Venue
Journal of Nursing Management
Published
2005-01
Volume
13
Issue
1
Pages
40-50
ISSN
  • 0966-0429 (ISSN-L)
  • 1365-2834
OpenAlex
W2028348762
Cited by
200
References
2
FWCI
11.74
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Nurse job satisfaction and retention: comparing public to private hospitals in Jordan. https://app.cochat.ai/papers/10.1111/j.1365-2834.2004.00453.x
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MRAYYAN, M. T. (2005). Nurse job satisfaction and retention: comparing public to private hospitals in Jordan. Journal of Nursing Management. https://doi.org/10.1111/j.1365-2834.2004.00453.x

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.