Browsing by Author "Orr, Mark G."
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- Mass fatality preparedness among medical examiners/coroners in the United States: a cross-sectional studyGershon, Robyn R.M.; Orr, Mark G.; Zhi, Qi; Merrill, Jacqueline A. (BMC, 2014)Background: In the United States (US), Medical Examiners and Coroners (ME/Cs) have the legal authority for the management of mass fatality incidents (MFI). Yet, preparedness and operational capabilities in this sector remain largely unknown. The purpose of this study was twofold; first, to identify appropriate measures of preparedness, and second, to assess preparedness levels and factors significantly associated with preparedness. Methods: Three separate checklists were developed to measure different aspects of preparedness: MFI Plan Elements, Operational Capabilities, and Pre-existing Resource Networks. Using a cross-sectional study design, data on these and other variables of interest were collected in 2014 from a national convenience sample of ME/C using an internet-based, anonymous survey. Preparedness levels were determined and compared across Federal Regions and in relation to the number of Presidential Disaster Declarations, also by Federal Region. Bivariate logistic and multivariable models estimated the associations between organizational characteristics and relative preparedness. Results: A large proportion (42%) of respondents reported that less than 25 additional fatalities over a 48-hour period would exceed their response capacities. The preparedness constructs measured three related, yet distinct, aspects of preparedness, with scores highly variable and generally suboptimal. Median scores for the three preparedness measures also varied across Federal Regions and as compared to the number of Presidential Declared Disasters, also by Federal Region. Capacity was especially limited for activating missing persons call centers, launching public communications, especially via social media, and identifying temporary interment sites. The provision of staff training was the only factor studied that was significantly (positively) associated (p < .05) with all three preparedness measures. Although ME/Cs ranked local partners, such as Offices of Emergency Management, first responders, and funeral homes, as the most important sources of assistance, a sizeable proportion (72%) expected federal assistance. Conclusions: The three measures of MFI preparedness allowed for a broad and comprehensive assessment of preparedness. In the future, these measures can serve as useful benchmarks or criteria for assessing ME/Cs preparedness. The study findings suggest multiple opportunities for improvement, including the development and implementation of national strategies to ensure uniform standards for MFI management across all jurisdictions.
- The Theory of Reasoned Action as Parallel Constraint Satisfaction: Towards a Dynamic Computational Model of Health BehaviorOrr, Mark G.; Thrush, Roxanne; Plaut, David C. (PLOS, 2013-05-03)The reasoned action approach, although ubiquitous in health behavior theory (e.g., Theory of Reasoned Action/Planned Behavior), does not adequately address two key dynamical aspects of health behavior: learning and the effect of immediate social context (i.e., social influence). To remedy this, we put forth a computational implementation of the Theory of Reasoned Action (TRA) using artificial-neural networks. Our model re-conceptualized behavioral intention as arising from a dynamic constraint satisfaction mechanism among a set of beliefs. In two simulations, we show that constraint satisfaction can simultaneously incorporate the effects of past experience (via learning) with the effects of immediate social context to yield behavioral intention, i.e., intention is dynamically constructed from both an individual’s pre-existing belief structure and the beliefs of others in the individual’s social context. In a third simulation, we illustrate the predictive ability of the model with respect to empirically derived behavioral intention. As the first known computational model of health behavior, it represents a significant advance in theory towards understanding the dynamics of health behavior. Furthermore, our approach may inform the development of population-level agent-based models of health behavior that aim to incorporate psychological theory into models of population dynamics.