Speaking up

Hall, Nicole; Klein, Valerie; Betts, Kristen; DeRanieri, Joseph T

doi:10.1097/01.numa.0000533774.22167.f2

Cited by 9 publications

(2 citation statements)

References 4 publications

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“…Despite the importance of reporting and/or speaking up about patient care issues and violation of nursing standards in the enhancement of patients' outcomes (Cole et al, 2019;Hall et al, 2018;Martinez et al, 2015;Okuyama et al, 2014;Vrbnjak et al, 2016), there is little in the literature assessing organisational silence among nurses. Besides, available studies about organisational silence in the nursing sector include small samples (<400), which could potentially affect the generalizability of the study findings, and tend to focus on underlying reasons that lead nurses to remain silent, rather than on individual nurse and hospital characteristics that explain this silence (Çaylak & Altuntas, 2017;Seren et al, 2018;Yalcin & Baykal, 2012;Yurdakul et al, 2016).…”

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confidence: 99%

Association between nurse and hospital characteristics and organisational silence behaviours in nurses: A cross‐sectional study

Labrague

2020

J. Nurs. Manag.

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Organisational silence is defined as situations where employees intentionally or unintentionally hold back information that might be valuable for their organisation (Bagheri, Zarei, & Aeen, 2012) and is categorized as either prosocial, defensive or acquiescent silence (Dyne, Ang, & Botero, 2003). Prosocial silence refers to the conscious decision of withholding information, ideas or opinions so as

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confidence: 99%

Association between nurse and hospital characteristics and organisational silence behaviours in nurses: A cross‐sectional study

Labrague

2020

J. Nurs. Manag.

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“…Turn-taking is the representative conversational feature; it occurs between two participants where each alternates speaking. Silence-breaking has a high correlation with the speaker's leadership (Hall et al, 2018). Self-turn is an indicator of an individual's active participation within a conversation, indicating a moment where they pause to think, breathe, or formulate thoughts before continuing their part of the conversation without interruption from others.…”

Section: B Conversational Feature Extractingmentioning

confidence: 99%

An Interpersonal Dynamics Analysis Procedure With Accurate Voice Activity Detection Using Low-Cost Recording Sensors

Lim,

Kang,

Choi

et al. 2024

IEEE Access

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Voice data is of special interest to organizational behavior researchers since it can be easily collected and provides a wealth of information for understanding interpersonal dynamics. Due to these benefits, voice activity detection (VAD) using wearable sensor badges has received considerable attention as one of the more objective and effective data-driven analysis methods rather than self-report methods for analyzing interpersonal dynamics. Moreover, with the VAD results, several prior works extracted conversational features, such as speaking times, turn-taking, and overlapped speaking, for attaining highlevel organizational insights. However, there is still room for improvement in the accuracy of the VAD models, and there is a lack of research to develop reliable conversational feature extracting algorithms in the previous works. Most of these prior studies relied on sociometric badges, which are costly electronic devices, including voice recording machines and redundant sensors. In this paper, we propose an interpersonal dynamics analysis procedure based on low-cost commercial recording sensors, consisting of data-driven VAD modeling and conversational feature extracting. To accurately identify voice presence, the VAD modeling incorporates three steps: signal preprocessing, data-derived generation, and VAD with machine learning-based classification and smoothing technique. We conducted experiments, from collecting datasets comprising the human verbal interaction of 15 three-person groups by commercial recording sensors to implementing our procedure on the datasets. Our results show that the proposed procedure excels in voice activity detection, achieving superior accuracy compared to prior studies. This remarkable accuracy subsequently ensures greater reliability in our conversational feature extraction. Thus, the proposed procedure can encourage organizational behavior researchers to acquire objective information about interpersonal dynamics and efficiently obtain high-level organizational insights with cost-effectiveness.

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