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Introduction In situ simulation training is a team-based training technique conducted on actual patient care units using equipment and resources from that unit, and involving actual members of the healthcare team. We describe our experience with in situ simulation training in a major children's medical centre. Materials and methods In situ simulations were conducted using standardised scenarios approximately twice per month on inpatient hospital units on a rotating basis. Simulations were scheduled so that each unit participated in at least two in situ simulations per year. Simulations were conducted on a revolving schedule alternating on the day and night shifts and were unannounced. Scenarios were preselected to maximise the educational experience, and frequently involved clinical deterioration to cardiopulmonary arrest. Results We performed 64 of the scheduled 112 (57%) in situ simulations on all shifts and all units over 21 months. We identified 134 latent safety threats and knowledge gaps during these in situ simulations, which we categorised as medication, equipment, and/or resource/system threats. Identification of these errors resulted in modification of systems to reduce the risk of error. In situ simulations also provided a method to reinforce teamwork behaviours, such as the use of assertive statements, role clarity, performance of frequent updating, development of a shared mental model, performance of independent double checks of high-risk medicines, and overcoming authority gradients between team members. Participants stated that the training programme was effective and did not disrupt patient care. Conclusions In situ simulations can identify latent safety threats, identify knowledge gaps, and reinforce teamwork behaviours when used as part of an organisation-wide safety programme. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to provide a comparative analysis of three different modeling approaches for exploring structural theories of violence. Specifically, ordinary least squares regression, geographically weighted regression and data envelopment analysis will be utilized to evaluate violent crime. This type of analysis expands upon traditional theory testing by deepening our understanding of differences in crime generation and its underlying demographic and socio-economic stimuli via different methodological lenses. A case study for the city of Cincinnati, Ohio is presented and the results suggest that a combination of approaches is likely the best strategy for evaluating violence in urban areas. [Copyright &y& Elsevier]

Over the past decade, a renewed interest in the analysis of crime hot-spots has emerged in the social and behavioral sciences. Spurred by improvements in computing power, data visualization and geographic information systems, numerous innovative approaches have materialized for identifying areas of elevated crime in urban environments. Unfortunately, many hot-spot analysis techniques treat the spatial and temporal aspects of crime as distinct entities, thus ignoring the necessary interaction of space and time to produce criminal opportunities. The purpose of this paper is to explore the utility of statistical measures for identifying and comparing the spatio-temporal footprints of robbery, burglary and assault. Functional and visual comparisons for spatio-temporal clusters are analyzed across a range of space–time values using a comprehensive database of crime events for Cincinnati, Ohio. Empirical results suggest that robbery, burglary and assault have dramatically different spatio-temporal signatures. [ABSTRACT FROM AUTHOR]

In the state of Ohio, House Bill 180, Senate Bill 175 and Senate Bill 5 collectively form Megan's Law. This law requires individuals convicted of a sexually oriented offense to register with the local sheriff `s office at least twenty days prior to the move-in date of the offender. Beginning July 31, 2003, Megan's Law prohibited an offender of a sexually oriented offense from establishing or occupying a residence within 1,000 feet of any school-related properties. A major social concern with this law and its subsequent enforcement is the belief that the geographic constraints placed on registered sex offenders effectively eliminate nearly all residential housing options. The purpose of this paper is to explore issues of geographic exclusion by conducting a spatial analysis of registered sex offenders in Hamilton County, Ohio. Analysis using geographic information systems (GIS), proximity evaluation and basic statistical techniques enables the impacts of this law to be assessed. [ABSTRACT FROM AUTHOR]