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Mendeley readers
Attention Score in Context
| Title |
Bayesian exponential random graph modelling of interhospital patient referral networks
|
|---|---|
| Published in |
Statistics in Medicine, April 2017
|
| DOI | 10.1002/sim.7301 |
| Pubmed ID | |
| Authors |
Alberto Caimo, Francesca Pallotti, Alessandro Lomi |
| Abstract |
Using original data that we have collected on referral relations between 110 hospitals serving a large regional community, we show how recently derived Bayesian exponential random graph models may be adopted to illuminate core empirical issues in research on relational coordination among healthcare organisations. We show how a rigorous Bayesian computation approach supports a fully probabilistic analytical framework that alleviates well-known problems in the estimation of model parameters of exponential random graph models. We also show how the main structural features of interhospital patient referral networks that prior studies have described can be reproduced with accuracy by specifying the system of local dependencies that produce - but at the same time are induced by - decentralised collaborative arrangements between hospitals. Copyright © 2017 John Wiley & Sons, Ltd. |
X Demographics
The data shown below were collected from the profiles of 6 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 Kingdom | 2 | 33% |
| Ireland | 2 | 33% |
| Singapore | 1 | 17% |
| Unknown | 1 | 17% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 67% |
| Scientists | 2 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 36 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 22% |
| Researcher | 5 | 14% |
| Student > Ph. D. Student | 5 | 14% |
| Student > Doctoral Student | 3 | 8% |
| Other | 2 | 6% |
| Other | 7 | 19% |
| Unknown | 6 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Social Sciences | 10 | 28% |
| Business, Management and Accounting | 3 | 8% |
| Medicine and Dentistry | 3 | 8% |
| Nursing and Health Professions | 3 | 8% |
| Computer Science | 3 | 8% |
| Other | 7 | 19% |
| Unknown | 7 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 23 April 2017.
All research outputs
#6,535,605
of 23,301,510 outputs
Outputs from Statistics in Medicine
#859
of 3,843 outputs
Outputs of similar age
#103,424
of 311,129 outputs
Outputs of similar age from Statistics in Medicine
#11
of 36 outputs
Altmetric has tracked 23,301,510 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 3,843 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done well, scoring higher than 77% 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 311,129 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.