On‐the‐job learning in the nursing profession

Abstract

Purpose The purpose of this paper is to gain more insight into employees' on‐the‐job learning. Its specific purpose is to develop and validate a classification of on‐the‐job learning activities and learning themes, focusing on the nursing profession in particular. Design/methodology/approach Two successive studies were conducted for this purpose. In the first study in‐depth interviews with 20 Dutch nurses were analysed using a grounded theory approach. The content validity of the categories found in the first study was investigated in the second study by interviewing 17 supervisors and eight educators from different hospitals in The Netherlands. Findings The paper finds that the main categories of learning activities are: learning by doing one's regular job, learning by applying something new in the job, learning by social interaction with colleagues, learning by theory or supervision, and learning by reflection. First‐order learning activities and second‐order learning activities can be distinguished. The main categories of on‐the‐job learning themes are: the technical‐practical domain, the socio‐emotional domain, the organisational domain, the developmental domain, and a pro‐active attitude to work. Research limitations/implications The validation study was conducted by the same researchers as the first study. The findings are based on one profession (nursing) in one country (The Netherlands). Practical implications The categories can be used by nurse educators and health sector managers/trainers to develop comprehensive and structured intervention methods for the improvement of on‐the‐job learning which do justice to the complexity and diversity of on‐the‐job learning by nurses. HR (development) professionals can use the classification as part of a competence management and development system. Originality/value The study provides a detailed, complete and multi‐dimensional explication of nurses' on‐the‐job learning activities and learning themes, grounding the classification and framework in empirical data and using multiple data sources.

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