Incivility in the context of diagnostic safety: a theoretical analysis
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Ana Lorena Hermosilla
Abstract
Objectives
To analyze a theoretical framework that incorporates incivility among healthcare teams in relation to diagnostic safety, and to assess its potential utility for advancing research and practice.
Methods
A structured literature review was conducted across PubMed, Web of Science, and CINAHL using search terms such as “diagnostic safety,” “team dynamics,” “theoretical framework,” “incivility,” and “diagnostic framework”. A theoretical framework was selected for analysis. Walker and Avant’s six-step theory analysis method was applied to assess the framework’s origins, meaning, logical adequacy, usefulness, generalizability, parsimony, and testability.
Results
The literature review yielded 144 articles after screening and applying inclusion criteria. Ten articles were reviewed, and the Model of Ward Team Dynamics in Diagnosis by Choi et al. was selected for analysis, as it was the only framework that explicitly addressed unacceptable behaviors (i.e., incivility) in diagnostic team settings. The Choi et al. framework uniquely integrates unacceptable behaviors as a mediating factor in diagnostic team performance, distinguishing it from other diagnostic models. The analysis found the framework to be conceptually well-grounded, with several constructs operationally defined and empirically measurable. However, gaps were identified in parsimony and the clarity of relational statements, indicating opportunities for refinement and empirical testing.
Conclusions
The framework offers a valuable theoretical foundation for studying the impact of incivility on diagnostic safety. Its integration of behavioral constructs supports its relevance for empirical research and intervention development aimed at improving team dynamics and diagnostic outcomes.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The author states no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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