Proposing and developing the definition and conceptual framework for healthcare resilience to cope with disasters

Building healthcare resilience is an important step towards creating more resilient communities to better cope with future disasters. To date, however, there appears to be little literature on how the concept of healthcare resilience should be defined and operationalized with a conceptual framework. This article aims to build a comprehensive healthcare disaster management approach guided by the concept of resilience. Methods: Google and major health electronic databases were searched to retrieve critical relevant publications. A total of 61 related publications were included, to provide a comprehensive overview of theories and definitions relevant to disaster resilience. Results and Discussions: Resilience is an inherent and adaptive capacity to cope with future uncertainty, through multiple strategies with all hazards approaches, in an attempt to achieve a positive outcome with linkage and cooperation. Healthcare resilience can be defined as the capability of healthcare organisations to resist, absorb, and respond to the shock of disasters while maintaining the most essential functions, then recover to their original state or adapt to a new state. It can be assessed by criteria, namely: robustness, redundancy, resourcefulness; and a complex of key dimensions, namely: vulnerability and safety, disaster resources and preparedness, continuity of essential health services, recovery and adaptation. Conclusions: This new concept places healthcare organisations’ disaster capabilities, management tasks, activities and disaster outcomes together into a comprehensive whole view, using an integrated approach and establishing achievable goals.

Impact of a new mandatory reporting law on reporting and identification of child sexual abuse: A seven year time trend analysis

Child sexual abuse is widespread and difficult to detect. To enhance case identification, many societies have enacted mandatory reporting laws requiring designated professionals, most often police, teachers, doctors and nurses, to report suspected cases to government child welfare agencies. Little research has explored the effects of introducing a reporting law on the number of reports made, and the outcomes of those reports. This study explored the impact of a new legislative mandatory reporting duty for child sexual abuse in the State of Western Australia over seven years. We analysed data about numbers and outcomes of reports by mandated reporters, for periods before the law (2006-08) and after the law (2009-12). Results indicate that the number of reports by mandated reporters of suspected child sexual abuse increased by a factor of 3.7, from an annual mean of 662 in the three year pre-law period to 2448 in the four year post-law period. The increase in the first two post-law years was contextually and statistically significant. Report numbers stabilised in 2010-12, at one report per 210 children. The number of investigated reports increased threefold, from an annual mean of 451 in the pre-law period to 1363 in the post-law period. Significant decline in the proportion of mandated reports that were investigated in the first two post-law years suggested the new level of reporting and investigative need exceeded what was anticipated. However, a subsequent significant increase restored the pre-law proportion, suggesting systemic adaptive capacity. The number of substantiated investigations doubled, from an annual mean of 160 in the pre-law period to 327 in the post-law period, indicating twice as many sexually abused children were being identified.

Multi-objective models and techniques for analysing the absolute capacity of railway networks

Railway capacity determination and expansion are very important topics. In prior research, the competition between different entities such as train services and train types, on different network corridors however have been ignored, poorly modelled, or else assumed to be static. In response, a comprehensive set of multi-objective models have been formulated in this article to perform a trade-off analysis. These models determine the total absolute capacity of railway networks as the most equitable solution according to a clearly defined set of competing objectives. The models also perform a sensitivity analysis of capacity with respect to those competing objectives. The models have been extensively tested on a case study and their significant worth is shown. The models were solved using a variety of techniques however an adaptive E constraint method was shown to be most superior. In order to identify only the best solution, a Simulated Annealing meta-heuristic was implemented and tested. However a linearization technique based upon separable programming was also developed and shown to be superior in terms of solution quality but far less in terms of computational time.