| Title |
Dissecting racial bias in an algorithm used to manage the health of populations
|
|---|---|
| Published in |
Science, October 2019
|
| DOI | 10.1126/science.aax2342 |
| Pubmed ID | |
| Authors |
Ziad Obermeyer, Brian Powers, Christine Vogeli, Sendhil Mullainathan |
X Demographics
The data shown below were collected from the profiles of 2,235 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 886 | 40% |
| United Kingdom | 136 | 6% |
| Canada | 48 | 2% |
| Spain | 37 | 2% |
| Japan | 32 | 1% |
| France | 29 | 1% |
| Germany | 28 | 1% |
| Australia | 24 | 1% |
| Italy | 17 | <1% |
| Other | 173 | 8% |
| Unknown | 825 | 37% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1379 | 62% |
| Scientists | 626 | 28% |
| Practitioners (doctors, other healthcare professionals) | 162 | 7% |
| Science communicators (journalists, bloggers, editors) | 67 | 3% |
| Unknown | 1 | <1% |
Mendeley readers
The data shown below were compiled from readership statistics for 2,174 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2174 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 309 | 14% |
| Researcher | 257 | 12% |
| Student > Master | 223 | 10% |
| Student > Bachelor | 179 | 8% |
| Student > Doctoral Student | 121 | 6% |
| Other | 359 | 17% |
| Unknown | 726 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 256 | 12% |
| Computer Science | 193 | 9% |
| Social Sciences | 152 | 7% |
| Engineering | 110 | 5% |
| Business, Management and Accounting | 76 | 3% |
| Other | 548 | 25% |
| Unknown | 839 | 39% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4482. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 April 2024.
All research outputs
#988
of 25,750,437 outputs
Outputs from Science
#53
of 83,295 outputs
Outputs of similar age
#10
of 377,167 outputs
Outputs of similar age from Science
#2
of 931 outputs
Altmetric has tracked 25,750,437 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 83,295 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 66.0. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 377,167 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 931 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.