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Clinical Learning Environment Inventory: factor analysis

Authors

  • Jennifer M. Newton,

    1. Jennifer M. Newton BA EdD RN Senior Research Fellow School of Nursing & Midwifery, Faculty of Medicine, Nursing & Health Sciences, Monash University, Frankston, Victoria, Australia
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  • Brian C. Jolly,

    1. Brian C. Jolly BSc MA(Ed) PhD Director Centre for Medical and Health Sciences Education, Monash University, Notting Hill, Victoria, Australia
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  • Cherene M. Ockerby,

    1. Cherene M. Ockerby BA Research Assistant Southern Health, Clayton, Victoria, Australia
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  • Wendy M. Cross

    1. Wendy M. Cross BAppSc MEd PhD Head School of Nursing & Midwifery, Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, Victoria, Australia
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J.M. Newton: e-mail: jenny.newton@med.monash.edu.au

Abstract

newton j.m., jolly b.c., ockerby c.m. & cross w.m. (2010) Clinical Learning Environment Inventory: factor analysis. Journal of Advanced Nursing66(6), 1371–1381.

Abstract

Title. Clinical Learning Environment Inventory: factor analysis.

Aim.  This paper is a report of the psychometric testing of the Clinical Learning Environment Inventory.

Background.  The clinical learning environment is a complex socio-cultural entity that offers a variety of opportunities to engage or disengage in learning. The Clinical Learning Environment Inventory is a self-report instrument consisting of 42 items classified into six scales: personalization, student involvement, task orientation, innovation, satisfaction and individualization. It was developed to examine undergraduate nursing students’ perceptions of the learning environment whilst on placement in clinical settings.

Method.  As a component of a longitudinal project, Bachelor of Nursing students (n = 659) from two campuses of a university in Australia, completed the Clinical Learning Environment Inventory from 2006 to 2008. Principal components analysis using varimax rotation was conducted to explore the factor structure of the inventory.

Results.  Data for 513 students (77%) were eligible for inclusion. Constraining data to a 6-factor solution explained 51% of the variance. The factors identified were: student-centredness, affordances and engagement, individualization, fostering workplace learning, valuing nurses’ work, and innovative and adaptive workplace culture. These factors were reviewed against recent theoretical developments in the literature.

Conclusion.  The study offers an empirically based and theoretically informed extension of the original Clinical Learning Environment Inventory, which had previously relied on ad hoc clustering of items and the use of internal reliability of its sub-scales. Further research is required to establish the consistency of these new factors.

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