SHIMAZAKI KanDepartment of Human Factors Engineering and Environmental Design Associate Professor |
Objectives: Large-scale earthquakes have affected many older people and persons with disabilities. When a disaster occurs, it is crucial to reduce the damage by taking measures in the fields of health care and welfare for those who require special care and consideration. Therefore, in the 2016 Kumamoto earthquake, this study aimed to organise the issues and measures related to disaster response by governmental and welfare-related organisations that provided healthcare and welfare services or information to people requiring attention by qualitative study through interview surveys.
Methods: We conducted a qualitative study through a semi-structured interview between the end of August 2018 and the end of February 2019. The target organisations were governmental, welfare-related, educational, information, and international exchange institutions. Survey items include the actual conditions, issues and countermeasures of the following four points: 1) the confirmation of the safety of those who need attention, 2) information sharing and support providers regarding healthcare and welfare services, 3) the status of cooperation with other departments and organisations, and 4) general and welfare shelters. We used thematic analysis to extract the issues and countermeasures.
Results: There were 20 respondents, including persons in the administrative position and professionals in 12 target governmental and welfare-related institutions or departments. We found four themes of issues and countermeasures regarding disaster response: “response within the governmental organisations”, “response of the governmental agencies, private organisations and community people”, “self-help”, and “welfare shelters”. Six sub-themes of issues were categorised: “vulnerability of information sharing system”, “insufficient manpower of governmental agency staff”, “unclear division of roles between public assistance, mutual assistance and private assistance”, “insufficient self-help”, “lack of understanding needs of various people needing attention”, and “insufficient number of welfare shelters”. We extracted seven sub-themes of the measures: “information sharing and strengthening of collaboration between other organisations and departments”, “utilisation of support from outside the disaster areas”, “application of mutual consist and private assistance”, “support for strengthening self-help”, “setting of welfare shelters that meet the needs of people requiring special needs”, “accepting people requiring special care in general evacuation centres”, and “providing various evacuation sites”.
Conclusion: In the future, our findings should be promoted: 1) expanding cooperation with governmental and private organisations, including information agencies such as radio stations, and community people in other areas as well, 2) building a well-timed back-end support system from outside the disaster areas, and 3) setting of evacuation centres and sites that match the needs of those who need attention.
In order to conduct regional disaster prevention activities continuously and appropriately, it is necessary to evaluate the effectiveness and appropriateness, and make the improvements based on evaluation. However, there are insufficient human resources of evaluators and it is difficult to foster the talent, since extensive field experience and broad disaster prevention knowledge is required for appropriate evaluation. This study constructed a prototype of a machine learning system for automatically evaluating regional disaster prevention activities. It also performs the machine learning with the data from activity records of the Bosai Contest as input variables, and the winning judgment data evaluated by the expert's review committee as output variables. As a result, the same result as experts' judgment was obtained with the probability of 94% for learning data and 79% for verification data.