Eun-Young Shim
Biographic Data
| ID | 1733745 |
|---|---|
| NAME | Eun-Young Shim |
| GIVEN NAMES | Eun-Young |
| FAMILY NAME | Shim |
| SIGNATURE | SHIM E |
| AFFILIATIONS | Amazon (United States) |
| ORCID | 0000-0002-0730-2955 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 7 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 1 |
Two Indias
We visited 1519 villages across 19 Indian states in 2009 to (a) count all health care providers and (b) elicit their quality as measured through tests of medical knowledge. We document three main findings. First, 75% of villages have at least one health care provider and 64% of care is sought in villages with 3 or more providers. Most providers are in the private sector (86%) and, within the private sector, the majority are 'informal providers' w…
Public Trauma after the Sewol Ferry Disaster
The Sewol ferry disaster severely shocked Korean society. The objective of this study was to explore how the public mood in Korea changed following the Sewol disaster using Twitter data. Data were collected from daily Twitter posts from 1 January 2011 to 31 December 2013 and from 1 March 2014 to 30 June 2014 using natural language-processing and text-mining technologies. We investigated the emotional utterances in reaction to the disaster by anal…
Two Indias
We visited 1519 villages across 19 Indian states in 2009 to (a) count all health care providers and (b) elicit their quality as measured through tests of medical knowledge. We document three main findings. First, 75% of villages have at least one health care provider and 64% of care is sought in villages with 3 or more providers. Most providers are in the private sector (86%) and, within the private sector, the majority are 'informal providers' w…
Public Trauma after the Sewol Ferry Disaster
The Sewol ferry disaster severely shocked Korean society. The objective of this study was to explore how the public mood in Korea changed following the Sewol disaster using Twitter data. Data were collected from daily Twitter posts from 1 January 2011 to 31 December 2013 and from 1 March 2014 to 30 June 2014 using natural language-processing and text-mining technologies. We investigated the emotional utterances in reaction to the disaster by anal…
Two Indias
We visited 1519 villages across 19 Indian states in 2009 to (a) count all health care providers and (b) elicit their quality as measured through tests of medical knowledge. We document three main findings. First, 75% of villages have at least one health care provider and 64% of care is sought in villages with 3 or more providers. Most providers are in the private sector (86%) and, within the private sector, the majority are 'informal providers' w…
Medicine (2 works) · Population (2 works) · Anger (1 work) · Business (1 work) · Criminology (1 work) · Data-Driven Disease Surveillance (1 work) · Demography (1 work) · Demography (1 work) · Disaster Management and Resilience (1 work) · Economic growth (1 work)