Prehospital & Disaster Medicine
Volume 29 – Issue 06 – December 2014
https://journals.cambridge.org/action/displayIssue?jid=PDM&tab=currentissue

Editorial
Ebola: Who is Responsible for the Political Failures?
Prehospital and Disaster Medicine / Volume 29 / Issue 06 / December 2014, pp 553 – 554
DOI: http://dx.doi.org/10.1017/S1049023X14001307
Published online: 17 December 2014

Original Research
Factors Associated with the Intention of Health Care Personnel to Respond to a Disaster
Susan B. Connor
Abstract
Introduction Over the past decade, numerous groups of researchers have studied the willingness of health care personnel (HCP) to respond when a disaster threatens the health of a community. Not one of those studies reported that 100% of HCP were willing to work during a public-health event (PHE).
Problem The objective of this study was to explore factors associated with the intent of HCP to respond to a future PHE.
Methods The theory of planned behavior (TPB) framed this cross-sectional study. Data were obtained via a web-based survey from 305 HCP. Linear associations between the TPB-based predictor and outcome variables were examined using Pearson’s correlations. Differences between two groups of HCP were calculated using independent t tests. A model-generating approach was used to develop and assess a series of TBP-based observed variable structural equation models for prediction of intent to respond to a future PHE and to explore moderating and mediating effects.
Results The beginning patterns of relationships identified by the correlation matrix and t tests were evident in the final structural equation model, even though the patterns of prediction differed from those posited by the theory. Outcome beliefs had both a significant, direct effect on intention and an indirect effect on intention that was mediated by perceived behavioral control. Control beliefs appeared to influence intention through perceived behavioral control, as posited by the TPB, and unexpectedly through subjective norm. Subjective norm not only mediated the relationship between control beliefs and intention, but also the relationship between referent beliefs and intention. Additionally, professional affiliation seemed to have a moderating effect on intention.
Conclusion The intention to respond was influenced primarily by normative and control factors. The intent of nurses to respond to a future PHE was influenced most by the control factors, whereas the intent of other HCP was shaped more by the normative factors. Health care educators can bolster the normative and control factors through education by focusing on team building and knowledge related to accessing supplies and support needed to respond when a disaster occurs.

Special Reports
Mass-gathering Health Research Foundational Theory: Part 1 – Population Models for Mass Gatherings
Adam Lunda1a2 c1, Sheila A. Turrisa2a3a4, Ron Bowlesa2, Malinda Steenkampa5, Alison Huttona5, Jamie Ransea6 and Paul Arbona5
a1 Department of Emergency Medicine, University of British Columbia, Vancouver, British Columbia, Canada
a2 Justice Institute of British Columbia, New Westminster, British Columbia, Canada
a3 Department of Emergency Medicine, University of British Columbia, Vancouver, British Columbia, Canada
a4 School of Nursing, University of Victoria, Victoria, British Columbia, Canada
a5 Flinders University, World Health Organization Collaborating Centre for Mass Gatherings and High Consequence/High Visibility Events, Flinders University of South Australia, Adelaide, South Australia, Australia
a6 University of Canberra, Faculty of Health, Bruce, Australian Capital Territory, Australia
Abstract
Background
The science underpinning the study of mass-gathering health (MGH) is developing rapidly. Current knowledge fails to adequately inform the understanding of the science of mass gatherings (MGs) because of the lack of theory development and adequate conceptual analysis. Defining populations of interest in the context of MGs is required to permit meaningful comparison and meta-analysis between events.
Process
A critique of existing definitions and descriptions of MGs was undertaken. Analyzing gaps in current knowledge, the authors sought to delineate the populations affected by MGs, employing a consensus approach to formulating a population model. The proposed conceptual model evolved through face-to-face group meetings, structured breakout sessions, asynchronous collaboration, and virtual international meetings.
Findings and Interpretation
Reporting on the incidence of health conditions at specific MGs, and comparing those rates between and across events, requires a common understanding of the denominators, or the total populations in question. There are many, nested populations to consider within a MG, such as the population of patients, the population of medical services providers, the population of attendees/audience/participants, the crew, contractors, staff, and volunteers, as well as the population of the host community affected by, but not necessarily attending, the event.
A pictorial representation of a basic population model was generated, followed by a more complex representation, capturing a global-health perspective, as well as academically- and operationally-relevant divisions in MG populations.
Conclusions
Consistent definitions of MG populations will support more rigorous data collection. This, in turn, will support meta-analysis and pooling of data sources internationally, creating a foundation for risk assessment as well as illness and injury prediction modeling. Ultimately, more rigorous data collection will support methodology for evaluating health promotion, harm reduction, and clinical-response interventions at MGs. Delineating MG populations progresses the current body of knowledge of MGs and informs the understanding of the full scope of their health effects.

Special Reports
Mass-gathering Health Research Foundational Theory: Part 2 – Event Modeling for Mass Gatherings
Sheila A. Turrisa1a2a3, Adam Lunda1a3 c1, Alison Huttona4, Ron Bowlesa3, Elizabeth Ellersona4, Malinda Steenkampa4, Jamie Ransea5 and Paul Arbona4
a1 Department of Emergency Medicine, University of British Columbia, Vancouver, British Columbia, Canada
a2 School of Nursing, University of Victoria, Victoria, British Columbia, Canada
a3 Justice Institute of British Columbia, New Westminster, British Columbia, Canada
a4 Flinders University, World Health Organization Collaborating Centre for Mass Gatherings and High Consequence/High Visibility Events, Flinders University of South Australia, Adelaide, South Australia, Australia
a5 University of Canberra, Faculty of Health, Bruce, Australian Capital Territory, Australia
Abstract
Background
Current knowledge about mass-gathering health (MGH) fails to adequately inform the understanding of mass gatherings (MGs) because of a relative lack of theory development and adequate conceptual analysis. This report describes the development of a series of event lenses that serve as a beginning “MG event model,” complimenting the “MG population model” reported elsewhere.
Methods
Existing descriptions of “MGs” were considered. Analyzing gaps in current knowledge, the authors sought to delineate the population of events being reported. Employing a consensus approach, the authors strove to capture the diversity, range, and scope of MG events, identifying common variables that might assist researchers in determining when events are similar and might be compared. Through face-to-face group meetings, structured breakout sessions, asynchronous collaboration, and virtual international meetings, a conceptual approach to classifying and describing events evolved in an iterative fashion.
Findings
Embedded within existing literature are a variety of approaches to event classification and description. Arising from these approaches, the authors discuss the interplay between event demographics, event dynamics, and event design. Specifically, the report details current understandings about event types, geography, scale, temporality, crowd dynamics, medical support, protective factors, and special hazards. A series of tables are presented to model the different analytic lenses that might be employed in understanding the context of MG events.
Interpretation
The development of an event model addresses a gap in the current body of knowledge vis a vis understanding and reporting the full scope of the health effects related to MGs. Consistent use of a consensus-based event model will support more rigorous data collection. This in turn will support meta-analysis, create a foundation for risk assessment, allow for the pooling of data for illness and injury prediction, and support methodology for evaluating health promotion, harm reduction, and clinical response interventions at MGs.