PurposeThe average mortality rate for death by suicide among OECD countries is 12.8 per 100000, and 33.5 for Korea. The present study analyzed big data extracted from Google to identify factors related to searches on suicide in Korea.Materials and MethodsGoogle search trends for the search words of suicide, stress, exercise, and drinking were obtained for 2004-2010. Analyzing data by month, the relationship between the actual number of suicides and search words per year was examined using multi-level models.ResultsBoth suicide rates and Google searches on suicide in Korea increased since 2007. An unconditional slope model indicated stress and suicide-related searches were positively related. A conditional model showed that factors associated with suicide by year directly affected suicide-related searches. The interaction between stress-related searches and the actual number of suicides was significant.ConclusionA positive relationship between stress- and suicide-related searches further confirmed that stress affects suicide. Taken together and viewed in context of the big data analysis, our results point to the need for a tailored prevention program. Real-time big data can be of use in indicating increases in suicidality when search words such as stress and suicide generate greater numbers of hits on portals and social network sites.

ObjectivesThis article reviews an evaluation vector model driven from a participatory action research leveraging a collective inquiry system named SMILE (Stanford Mobile Inquiry-based Learning Environment).MethodsSMILE has been implemented in a diverse set of collective inquiry generation and analysis scenarios including community health care-specific professional development sessions and community-based participatory action research projects. In each scenario, participants are given opportunities to construct inquiries around physical and emotional health-related phenomena in their own community.ResultsParticipants formulated inquiries as well as potential clinical treatments and hypothetical scenarios to address health concerns or clarify misunderstandings or misdiagnoses often found in their community practices. From medical universities to rural village health promotion organizations, all participatory inquiries and potential solutions can be collected and analyzed. The inquiry and solution sets represent an evaluation vector which helps educators better understand community health issues at a much deeper level.ConclusionsSMILE helps collect problems that are most important and central to their community health concerns. The evaluation vector, consisting participatory and collective inquiries and potential solutions, helps the researchers assess the participants' level of understanding on issues around health concerns and practices while helping the community adequately formulate follow-up action plans. The method used in SMILE requires much further enhancement with machine learning and advanced data visualization.