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Trust people you’ve never worked with: a social network visualization of teamwork, cohesion, social support, and mental health in NHS Covid personnel. [Dataset]

Contributors

Stefan Schilling
Data Collector

Maria Armaou
Data Collector

Zoe Morrison
Data Collector

Paul Carding
Data Collector

Martin Bricknell
Data Collector

Vincent Connelly
Data Collector

Abstract

The complexity, patient volume, and severity of COVID-19, exacerbated by already existing staff shortages in the healthcare sector, required an unprecedented upscaling of capacity during the peak phases of COVID-19. Hospitals around the world relied on the deployment of nurses, doctors, and allied health professionals to provide relief and support for overwhelmed and understaffed personnel in Intensive Care, Infectious Disease, and High Dependency Units (ICU, IDU, HDU). As many of those deployed had little to no prior experience or training in intensive, acute, or infectious disease (ID) care, such ad-hoc deployment of health-care workers (HCW) into COVID ICUs may have undermined many of the antecedents of inter-professional teamwork in healthcare teams. For example, research has repeatedly found substantial benefits of interprofessional/interdisciplinary (IP/ID) teamwork on staff well-being and social support and was linked to improved integrated care and patient outcomes, patient satisfaction, as well as reduced treatment costs, mortality rates, length of in-patient stay, and clinical error rates. However, effective IP/ID teams rely on prior relational coordination and establishment of shared mental models, something that the rapid and often fluid amalgamations of personnel from different professional backgrounds during COVID may not have had.

Citation

SCHILLING, S., ARMAOU, M., MORRISON, Z., CARDING, P., BRICKNELL, M. and CONNELLY, V. 2024. Trust people you’ve never worked with: a social network visualization of teamwork, cohesion, social support, and mental health in NHS Covid personnel. [Dataset]. Frontiers in psychology [online], 15, article number 1293171. Available from: https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1293171/full#supplementary-material

Acceptance Date Jan 16, 2024
Online Publication Date Feb 20, 2024
Publication Date Dec 31, 2024
Deposit Date Mar 15, 2024
Publicly Available Date Mar 15, 2024
Publisher Frontiers Media
DOI https://doi.org/10.3389/fpsyg.2024.1293171
Keywords Teamwork; Preparedness; Healthcare; Inter-professional; Leadership; Mental health; Inter-disciplinary; COVID-19
Public URL https://rgu-repository.worktribe.com/output/2271777
Related Public URLs https://rgu-repository.worktribe.com/output/2266075 (Journal article)
Type of Data PDF, XLSX files, JPG and accompanying TXT file.
Collection Date Dec 31, 2021
Collection Method This study adopted a qualitative deductive exploratory methodology, aimed at expanding upon the pre-existing theoretical knowledge by exploring the lived experience of HCWs during the COVID-19 pandemic. Considering the methodological difficulties of observing team processes during an active Highly Infectious Disease (HID) outbreak, semi-structured video-interviews were chosen to assess HCWs self-reported experiences, and evaluations of their teamwork with colleagues on COVID-19 wards. Two semi-structured interview guides were developed for: 1) frontline facing staff aimed at exploring HCWs perceptions, motivations, shared beliefs, values, and attitudes towards their group and their leaders during their work in IP/ID COVID-19 frontline teams; and 2) leaders (i.e., Clinical or Nursing Directors, Matrons, Senior Managers) aimed at exploring workforce allocation, ward management practices and unearth potential innovations and best practices (The semi-structured interview guides are Data-sheet 1 and 2). These interview guides were designed based on the results from a systematic review of the available scientific evidence on teamwork in ad-hoc, fluid, IP/ID healthcare teams during crisis situations (Schilling et al., 2022) and pilot interviews with medical, nursing, and allied health professionals to gain a preliminary understanding of the issues and experiences faced by HCWs during COVID-19 work. Interview data were analyzed using a sequential Thematic Network Analysis approach, which used network graph modeling to supplement thematic analysis of qualitative interview data. While most thematic analyses are restricted to summary description of the qualitative data, the utilization of network graph modeling permits the added benefit of exploring the inherent structure between themes in a form that is transparent of the research process and replicable by other researchers, without neglecting the qualitative nature of the data. Additionally, by utilizing network metrics, (e.g., weighted degree or modularity), the importance of particular themes, the relationships between themes and the potential thematic clustering of themes can be illustrated and further analyzed by showing consistency of themes across different samples (e.g., deployed vs. permanent personnel). Alongside the "rich description" of the participants voice which allows some insight into potential pathways, the visualization of the textual data allows for both increased transparency about the analytic process and the differences between participant groups as well as improved reproducibility.

Files

SCHILLING 2024 Trust people (DATASET) (478 Kb)
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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/

Copyright Statement
© 2024 Schilling, Armaou, Morrison, Carding, Bricknell and Connelly. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.





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