FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2897768025> ?p ?o ?g. }

• W2897768025 endingPage "e10179" @default.
• W2897768025 startingPage "e10179" @default.
• W2897768025 abstract "India is home to 20% of the world's suicide deaths. Although statistics regarding suicide in India are distressingly high, data and cultural issues likely contribute to a widespread underreporting of the problem. Social stigma and only recent decriminalization of suicide are among the factors hampering official agencies' collection and reporting of suicide rates.As the product of a data collaborative, this paper leverages private-sector search engine data toward gaining a fuller, more accurate picture of the suicide issue among young people in India. By combining official statistics on suicide with data generated through search queries, this paper seeks to: add an additional layer of information to more accurately represent the magnitude of the problem, determine whether search query data can serve as an effective proxy for factors contributing to suicide that are not represented in traditional datasets, and consider how data collaboratives built on search query data could inform future suicide prevention efforts in India and beyond.We combined official statistics on demographic information with data generated through search queries from Bing to gain insight into suicide rates per state in India as reported by the National Crimes Record Bureau of India. We extracted English language queries on suicide, depression, hanging, pesticide, and poison. We also collected data on demographic information at the state level in India, including urbanization, growth rate, sex ratio, internet penetration, and population. We modeled the suicide rate per state as a function of the queries on each of the 5 topics considered as linear independent variables. A second model was built by integrating the demographic information as additional linear independent variables.Results of the first model fit (R2) when modeling the suicide rates from the fraction of queries in each of the 5 topics, as well as the fraction of all suicide methods, show a correlation of about 0.5. This increases significantly with the removal of 3 outliers and improves slightly when 5 outliers are removed. Results for the second model fit using both query and demographic data show that for all categories, if no outliers are removed, demographic data can model suicide rates better than query data. However, when 3 outliers are removed, query data about pesticides or poisons improves the model over using demographic data.In this work, we used search data and demographics to model suicide rates. In this way, search data serve as a proxy for unmeasured (hidden) factors corresponding to suicide rates. Moreover, our procedure for outlier rejection serves to single out states where the suicide rates have substantially different correlations with demographic factors and query rates." @default.
• W2897768025 created "2018-10-26" @default.
• W2897768025 creator A5003579441 @default.
• W2897768025 creator A5020744129 @default.
• W2897768025 creator A5035590931 @default.
• W2897768025 creator A5038498641 @default.
• W2897768025 creator A5039448366 @default.
• W2897768025 creator A5059787882 @default.
• W2897768025 creator A5069885186 @default.
• W2897768025 creator A5082372443 @default.
• W2897768025 date "2019-01-04" @default.
• W2897768025 modified "2023-09-25" @default.
• W2897768025 title "How Search Engine Data Enhance the Understanding of Determinants of Suicide in India and Inform Prevention: Observational Study" @default.
• W2897768025 cites W1596222673 @default.
• W2897768025 cites W1964286138 @default.
• W2897768025 cites W1973882848 @default.
• W2897768025 cites W1989509557 @default.
• W2897768025 cites W2003834798 @default.
• W2897768025 cites W2007946274 @default.
• W2897768025 cites W2081492518 @default.
• W2897768025 cites W2083989195 @default.
• W2897768025 cites W2117239687 @default.
• W2897768025 cites W2129587854 @default.
• W2897768025 cites W2147887168 @default.
• W2897768025 cites W2151932005 @default.
• W2897768025 cites W2216940785 @default.
• W2897768025 cites W2259319253 @default.
• W2897768025 cites W2280159237 @default.
• W2897768025 cites W2329073810 @default.
• W2897768025 cites W2338898281 @default.
• W2897768025 cites W2339369942 @default.
• W2897768025 cites W2405042511 @default.
• W2897768025 cites W2553292362 @default.
• W2897768025 cites W2559678703 @default.
• W2897768025 cites W2588431269 @default.
• W2897768025 cites W2590358025 @default.
• W2897768025 cites W2611013695 @default.
• W2897768025 cites W2613309154 @default.
• W2897768025 cites W2800071573 @default.
• W2897768025 cites W2804904320 @default.
• W2897768025 cites W2828558326 @default.
• W2897768025 cites W2962848499 @default.
• W2897768025 cites W4249896530 @default.
• W2897768025 doi "https://doi.org/10.2196/10179" @default.
• W2897768025 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6682304" @default.
• W2897768025 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30609976" @default.
• W2897768025 hasPublicationYear "2019" @default.
• W2897768025 type Work @default.
• W2897768025 sameAs 2897768025 @default.
• W2897768025 citedByCount "18" @default.
• W2897768025 countsByYear W28977680252020 @default.
• W2897768025 countsByYear W28977680252021 @default.
• W2897768025 countsByYear W28977680252022 @default.
• W2897768025 countsByYear W28977680252023 @default.
• W2897768025 crossrefType "journal-article" @default.
• W2897768025 hasAuthorship W2897768025A5003579441 @default.
• W2897768025 hasAuthorship W2897768025A5020744129 @default.
• W2897768025 hasAuthorship W2897768025A5035590931 @default.
• W2897768025 hasAuthorship W2897768025A5038498641 @default.
• W2897768025 hasAuthorship W2897768025A5039448366 @default.
• W2897768025 hasAuthorship W2897768025A5059787882 @default.
• W2897768025 hasAuthorship W2897768025A5069885186 @default.
• W2897768025 hasAuthorship W2897768025A5082372443 @default.
• W2897768025 hasBestOaLocation W28977680251 @default.
• W2897768025 hasConcept C105795698 @default.
• W2897768025 hasConcept C133462117 @default.
• W2897768025 hasConcept C142724271 @default.
• W2897768025 hasConcept C198052957 @default.
• W2897768025 hasConcept C2776641880 @default.
• W2897768025 hasConcept C2908647359 @default.
• W2897768025 hasConcept C3017944768 @default.
• W2897768025 hasConcept C33923547 @default.
• W2897768025 hasConcept C41008148 @default.
• W2897768025 hasConcept C526869908 @default.
• W2897768025 hasConcept C545542383 @default.
• W2897768025 hasConcept C71924100 @default.
• W2897768025 hasConcept C99454951 @default.
• W2897768025 hasConceptScore W2897768025C105795698 @default.
• W2897768025 hasConceptScore W2897768025C133462117 @default.
• W2897768025 hasConceptScore W2897768025C142724271 @default.
• W2897768025 hasConceptScore W2897768025C198052957 @default.
• W2897768025 hasConceptScore W2897768025C2776641880 @default.
• W2897768025 hasConceptScore W2897768025C2908647359 @default.
• W2897768025 hasConceptScore W2897768025C3017944768 @default.
• W2897768025 hasConceptScore W2897768025C33923547 @default.
• W2897768025 hasConceptScore W2897768025C41008148 @default.
• W2897768025 hasConceptScore W2897768025C526869908 @default.
• W2897768025 hasConceptScore W2897768025C545542383 @default.
• W2897768025 hasConceptScore W2897768025C71924100 @default.
• W2897768025 hasConceptScore W2897768025C99454951 @default.
• W2897768025 hasIssue "1" @default.
• W2897768025 hasLocation W28977680251 @default.
• W2897768025 hasLocation W28977680252 @default.
• W2897768025 hasLocation W28977680253 @default.
• W2897768025 hasLocation W28977680254 @default.
• W2897768025 hasLocation W28977680255 @default.
• W2897768025 hasLocation W28977680256 @default.
• W2897768025 hasOpenAccess W2897768025 @default.

• next