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TwitterData on visits to physician offices, hospital outpatient departments and hospital emergency departments by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. Note that the data file available here has more recent years of data than what is shown in the PDF or Excel version. Data for 2017 physician office visits are not available. SOURCE: NCHS, National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey. For more information on the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey, see the corresponding Appendix entries at https://www.cdc.gov/nchs/data/hus/hus17_appendix.pdf.
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TwitterThis statistic shows a ranking of the estimated average number of physicians per 1,000 inhabitants in 2020 in Latin America, differentiated by country.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).
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License information was derived automatically
Comprehensive dataset containing 911 verified Free clinic businesses in United States with complete contact information, ratings, reviews, and location data.
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The CNN / DailyMail Dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering.
The BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.
For each instance, there is a string for the article, a string for the highlights, and a string for the id. See the CNN / Daily Mail dataset viewer to explore more examples.
{'id': '0054d6d30dbcad772e20b22771153a2a9cbeaf62',
'article': '(CNN) -- An American woman died aboard a cruise ship that docked at Rio de Janeiro on Tuesday, the same ship on which 86 passengers previously fell ill, according to the state-run Brazilian news agency, Agencia Brasil. The American tourist died aboard the MS Veendam, owned by cruise operator Holland America. Federal Police told Agencia Brasil that forensic doctors were investigating her death. The ship's doctors told police that the woman was elderly and suffered from diabetes and hypertension, according the agency. The other passengers came down with diarrhea prior to her death during an earlier part of the trip, the ship's doctors said. The Veendam left New York 36 days ago for a South America tour.'
'highlights': 'The elderly woman suffered from diabetes and hypertension, ship's doctors say .
Previously, 86 passengers had fallen ill on the ship, Agencia Brasil says .'}
The average token count for the articles and the highlights are provided below:
| Feature | Mean Token Count |
|---|---|
| Article | 781 |
| Highlights | 56 |
id: a string containing the heximal formated SHA1 hash of the url where the story was retrieved fromarticle: a string containing the body of the news article highlights: a string containing the highlight of the article as written by the article authorThe CNN/DailyMail dataset has 3 splits: train, validation, and test. Below are the statistics for Version 3.0.0 of the dataset.
| Dataset Split | Number of Instances in Split |
|---|---|
| Train | 287,113 |
| Validation | 13,368 |
| Test | 11,490 |
Version 1.0.0 aimed to support supervised neural methodologies for machine reading and question answering with a large amount of real natural language training data and released about 313k unique articles and nearly 1M Cloze style questions to go with the articles. Versions 2.0.0 and 3.0.0 changed the structure of the dataset to support summarization rather than question answering. Version 3.0.0 provided a non-anonymized version of the data, whereas both the previous versions were preprocessed to replace named entities with unique identifier labels.
The data consists of news articles and...
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TwitterThe Department of Veterans Affairs (VA) provides healthcare services to its veterans across the USA including territories and possessions. Healthcare services are delivered through 18 geographically divided administrative areas called Veterans Integrated Services Networks (VISN). Each VISN is divided into healthcare areas called Markets and Submarkets. Each Submarket is divided into Sectors and each Sector comprises one or more counties. In 1995 a process was created to coordinate and review the realignment of the Heath Care Networks. The Capital Asset Realignment for Enhanced Services (CARES) process established VISN 'subsets' called Markets, Submarkets and Sectors which, being smaller than VISNs, allowed for more precise analyses for greater access measurement to health care.
The County layer is the base geographic unit of the VISN-Market-Submarket-Sector-County hierarchy. The key attribute in this data set is the FIPS which is defined as a string of 5 characters with unique alphanumeric combinations for each site. The first 2 are the State FIPS code and the next 3 designate the County FIPS code. Example: '01031' is the FIPS for Coffee County, Alabama.
A Sector is a cluster of geographically adjacent counties within a VA Submarket. The process of aggregating counties into sectors uses a combination of automated algorithms and manual inspection of maps. The key attribute in this data set is the SECTOR which is defined as a string of eight characters broken down into four parts in the order of VISN (2-char), Market (1-char), Submarket (1-char), and Sector(1-char) connected by a hyphen. For example, Sector 12-a-3-A indicates VISN 12, Market a, Submarket 3 and Sector A.
Sub-markets reflect a clustering of the enrollee population within a market and are an aggregation of Sectors. The key attribute in this data set is the SUBMARKET which is defined as a string of six characters broken down in three parts in the order of VISN (2-char), Market (1-char), and Submarket (1-char) connected by a hyphen. For example, Submarket 12-a-3 indicates VISN 12, Market a, and Submarket 3.
CARES defines Markets as "an aggregated geographic area having a sufficient population and geographic size to both benefit from the coordination and planning of health care services and to support a full healthcare delivery system (i.e. primary care, mental health care, inpatient care, tertiary care, and long term care)". Each Market is built from Submarkets. The key attribute in this data set is the MARKET which is defined as a string of four characters broken down in two parts in the order of HCN (2-char) and Market (1-char) connected by a hyphen. For example, Market 12-a indicates VISN 12 and Market a.
The key attribute in the VISN data set is defined as a string of two characters from 01-23, excluding 3, 11, 13, 14 and 18; a VISN also has an officially recognized VA title. For example, VISN 06 is the Mid-Atlantic Health Care Network. VISNs can span across neighboring countries to include areas that are not contiguous. For example, VISN 08 includes Florida and Puerto Rico in addition to most of Florida and southern Georgia, and VISN 20 includes Alaska and parts of the northwest conterminous United States. Each VISN is built from Markets, Submarkets, Sectors and Counties derived from Census (2010) County data.
Because VISNs are composed of VHA markets, VISN boundaries align with the outer edges of their constituent markets’ boundaries. Markets cross state borders wherever it is necessary to keep outpatient clinics (e.g. Community-Based Outpatient Clinics(CBOCs)) and their catchment areas in the same market as their parent medical centers. Thus, VISN boundaries also cross state borders. In 2016 senior leadership considered the challenge of conforming VISN boundaries to MyVA Districts, which coincide with state boundaries. It was agreed that VHA would not separate outpatient clinics from their parent medical centers due to added complexity. Many outpatient providers hold clinics at their mother facilities and clinics are on the same health record as their parent facilities. VISN and market maps created by VHA Policy and Planning conform to these principals and are the official maps for VHA VISNs and markets.
While the Planning Systems Support Group (PSSG) develops the feature classes depicting the various VHA geographies, the PSSG does not have the authority to modify or reorganize the boundaries. The boundaries are developed at higher levels of the VHA and passed to the PSSG to be translated into spatial features.
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TwitterData on visits to physician offices, hospital outpatient departments and hospital emergency departments by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. Note that the data file available here has more recent years of data than what is shown in the PDF or Excel version. Data for 2017 physician office visits are not available. SOURCE: NCHS, National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey. For more information on the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey, see the corresponding Appendix entries at https://www.cdc.gov/nchs/data/hus/hus17_appendix.pdf.