Background: Health behavior (HB) is an action taken by a person who pursues good health and prevents illness. Health behavior, thus, reflects a person’s health beliefs and attracts, particularly, on published papers ...Background: Health behavior (HB) is an action taken by a person who pursues good health and prevents illness. Health behavior, thus, reflects a person’s health beliefs and attracts, particularly, on published papers in academics. However, who is the most influential author (MIA) with highly-cited papers on HB remains unknown. Objective: The purpose of this study is to apply the authorship-weighted scheme (AWS) developed by authors to select the MIA on HB using the visual displays on Google Maps. Methods: We obtained 1,116 abstracts published between 2012 and 2016 from Medline based on the keywords of (health [Title]) and (behavior [Title] or behavior [Title]) on September 22, 2018. The author names, countries/areas, and Pubmed paper IDs were recorded. The AWS was applied to (1) select the most productive authors (MPA) using social network analysis (SNA);(2) discover the MIA using h-indexes and author impact factors (AIF) dispersed on Google Maps, and (3) display the countries/areas distributed for the x-index in geography. Pajek software was performed to determine the partition categories of clusters. Results: We found that the MPA and MIA are Matthew K Nock (US) and Erika A Waters (US) for the MPA and MIA, respectively. All visual representations that are the form of a dashboard can be easily displayed on Google Maps. The most influential countries are the US (=19.03) and Australia (=6.46) with the highest x-indexes. Readers are suggested to manipulate them on their own on Google Maps. Conclusion: Many individual researchers achievements (IRA) were determined using h-index, AIF, x-index, or other bibliometric indices without quantifying author contributions. We demonstrated visualized representations on Google Maps using the AWS developed by authors to measure authors influences in a specific discipline. The research approach using the AWS to quantify the authors contributions can be applied to measure IRA in the future.展开更多
文摘Background: Health behavior (HB) is an action taken by a person who pursues good health and prevents illness. Health behavior, thus, reflects a person’s health beliefs and attracts, particularly, on published papers in academics. However, who is the most influential author (MIA) with highly-cited papers on HB remains unknown. Objective: The purpose of this study is to apply the authorship-weighted scheme (AWS) developed by authors to select the MIA on HB using the visual displays on Google Maps. Methods: We obtained 1,116 abstracts published between 2012 and 2016 from Medline based on the keywords of (health [Title]) and (behavior [Title] or behavior [Title]) on September 22, 2018. The author names, countries/areas, and Pubmed paper IDs were recorded. The AWS was applied to (1) select the most productive authors (MPA) using social network analysis (SNA);(2) discover the MIA using h-indexes and author impact factors (AIF) dispersed on Google Maps, and (3) display the countries/areas distributed for the x-index in geography. Pajek software was performed to determine the partition categories of clusters. Results: We found that the MPA and MIA are Matthew K Nock (US) and Erika A Waters (US) for the MPA and MIA, respectively. All visual representations that are the form of a dashboard can be easily displayed on Google Maps. The most influential countries are the US (=19.03) and Australia (=6.46) with the highest x-indexes. Readers are suggested to manipulate them on their own on Google Maps. Conclusion: Many individual researchers achievements (IRA) were determined using h-index, AIF, x-index, or other bibliometric indices without quantifying author contributions. We demonstrated visualized representations on Google Maps using the AWS developed by authors to measure authors influences in a specific discipline. The research approach using the AWS to quantify the authors contributions can be applied to measure IRA in the future.
