It's been a year since the police announced new measures to get more women into the force. One of those measures was a reality tv show, "Women In Blue", that followed seven female police officers on the job. Among them was Constable Bridget Suckling, who specialises in disaster victim identification. She juggles major operations such as Pike River and the aftermath of the Christchurch earthquakes with her work on the Auckland Search and rescue squad.She talks to Katy Gosset about why she joined the police and the impact of "Women in Blue".
Objective: The nature of disaster research makes it difficult to adequately measure the impact that significant events have on a population. Large, representative samples are required, ideally with comparable data collected before the event. When Christchurch, New Zealand, was struck by multiple, devastating earthquakes, there presented an opportunity to investigate the effects of dose-related quakes (none, one, two or three over a 9-month period) on the cognition of Canterbury’s elderly population through the New Zealand Brain Research Institute’s (NZBRI’s) cognitive screening study. The related effects of having a concomitant medical condition, sex, age and estimated- full scale IQ (Est-FSIQ) on cognition were also investigated. Method: 609 participants were tested on various neuropsychological tests and a self-rated dementia scale in a one hour interview at the NZBRI. Four groups were established, based on the number of major earthquakes experienced at the time of testing: “EQ-dose: None” (N = 51) had experienced no quakes; “EQ-dose: One” (N = 193) had experienced the initial quake in September 2010; “EQ-dose: Two” (N = 82) also experienced the most devastating February 2011 quake; and “EQ-dose: Three” (N = 265) also the June 2011 quake at testing. Results: Two neuropsychological variables of Trail A and the AD8 were impacted by an EQ-dose effect, while having a medical condition was associated with poorer function on the MoCA, Rey Copy and Recall, Trail A, and AD8. Having a major medical condition led to worse performance on the Rey Copy and Recall following the major February earthquake. Males performed significantly better on Trail A and Rey Planning, while females better on the MoCA. Older participants (>73) had significantly lower scores on the MoCA than younger participants (<74), while those with a higher Est-FSIQ (>111) had better scores on the MoCA and Rey Recall than participants with a lower Est-FSIQ. Finally, predicted variable analysis (based on calculated, sample-specific Z-scores) failed to find a significant earthquake effect when variables of age, sex and Est-FSIQ were controlled for, while there was a significant effect of medical condition on each measure. Conclusion: The current thesis provides evidence suggesting resilience amongst Canterbury’s elderly population in the face of the sequence of significant quakes that struck the region over a year from September 2010. By contrast, having a major medical condition was a ‘more significant life event’ in terms of impact on cognition in this group.
When disasters and crises, both man-made and natural, occur, resilient higher education institutions adapt in order to continue teaching and research. This may necessitate the closure of the whole institution, a building and/or other essential infrastructure. In disasters of large scale the impact can be felt for many years. There is an increasing recognition of the need for disaster planning to restructure educational institutions so that they become more resilient to challenges including natural disasters (Seville, Hawker, & Lyttle, 2012).The University of Canterbury (UC) was affected by seismic events that resulted in the closure of the University in September 2010 for 10 days and two weeks at the start of the 2011 academic year This case study research describes ways in which e-learning was deployed and developed by the University to continue and even to improve learning and teaching in the aftermath of a series of earthquakes in 2010 and 2011. A qualitative intrinsic embedded/nested single case study design was chosen for the study. The population was the management, support staff and educators at the University of Canterbury. Participants were recruited with purposive sampling using a snowball strategy where the early key participants were encouraged to recommend further participants. Four sources of data were identified: (1) documents such as policy, reports and guidelines; (2) emails from leaders of the colleges and academics; (3) communications from senior management team posted on the university website during and after the seismic activity of 2010 and 2011; and (4) semi-structured interviews of academics, support staff and members of senior management team. A series of inductive descriptive content analyses identified a number of themes in the data. The Technology Acceptance Model 2 (Venkatesh & Davis, 2000) and the Indicator of Resilience Model (Resilient Organisations, 2012) were used for additional analyses of each of the three cases. Within the University case, the cases of two contrasting Colleges were embedded to produce a total of three case studies describing e-learning from 2000 - 2014. One contrast was the extent of e-learning deployment at the colleges: The College of Education was a leader in the field, while the College of Business and Law had relatively little e-learning at the time of the first earthquake in September 2010. The following six themes emerged from the analyses: Communication about crises, IT infrastructure, Availability of e-learning technologies, Support in the use of e-learning technologies, Timing of crises in academic year and Strategic planning for e-learning. One of the findings confirmed earlier research that communication to members of an organisation and the general public about crises and the recovery from crises is important. The use of communication channels, which students were familiar with and already using, aided the dissemination of the information that UC would be using e-learning as one of the options to complete the academic year. It was also found that e-learning tools were invaluable during the crises and facilitated teaching and learning whilst freeing limited campus space for essential activities and that IT infrastructure was essential to e-learning. The range of e-learning tools and their deployment evolved over the years influenced by repeated crises and facilitated by the availability of centrally located support from the e-Learning support team for a limited set of tools, as well as more localised support and collaboration with colleagues. Furthermore, the reasons and/or rate of e-learning adoption in an educational institution during crises varied with the time of the academic year and the needs of the institution at the time. The duration of the crises also affected the adoption of e-learning. Finally, UC’s lack of an explicit e-learning strategy influenced the two colleges to develop college-specific e-learning plans and those College plans complemented the incorporation of e-learning for the first time in the University’s teaching and learning strategy in 2013. Twelve out of the 13 indicators of the Indicators of Resilience Model were found in the data collected for the study and could be explained using the model; it revealed that UC has become more resilient with e-learning in the aftermath of the seismic activities in 2010 and 2011. The interpretation of the results using TAM2 demonstrated that the adoption of technologies during crises aided in overcoming barriers to learning at the time of the crisis. The recommendations from this study are that in times of crises, educational institutions take advantage of Cloud computing to communicate with members of the institution and stakeholders. Also, that the architecture of a university’s IT infrastructure be made more resilient by increasing redundancy, backup and security, centralisation and Cloud computing. In addition, when under stress it is recommended that new tools are only introduced when they are essential.
