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Mendeley readers
Attention Score in Context
| Title |
Computational Psychometrics for Modeling System Dynamics during Stressful Disasters
|
|---|---|
| Published in |
Frontiers in Psychology, August 2017
|
| DOI | 10.3389/fpsyg.2017.01401 |
| Pubmed ID | |
| Authors |
Pietro Cipresso, Alessandro Bessi, Desirée Colombo, Elisa Pedroli, Giuseppe Riva |
| Abstract |
Disasters can be very stressful events. However, computational models of stress require data that might be very difficult to collect during disasters. Moreover, personal experiences are not repeatable, so it is not possible to collect bottom-up information when building a coherent model. To overcome these problems, we propose the use of computational models and virtual reality integration to recreate disaster situations, while examining possible dynamics in order to understand human behavior and relative consequences. By providing realistic parameters associated with disaster situations, computational scientists can work more closely with emergency responders to improve the quality of interventions in the future. |
X Demographics
The data shown below were collected from the profiles of 5 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 | 40% |
| Switzerland | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 40% |
| Practitioners (doctors, other healthcare professionals) | 2 | 40% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 64 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 17% |
| Student > Bachelor | 9 | 14% |
| Researcher | 7 | 11% |
| Student > Master | 7 | 11% |
| Student > Doctoral Student | 5 | 8% |
| Other | 9 | 14% |
| Unknown | 16 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 15 | 23% |
| Medicine and Dentistry | 5 | 8% |
| Nursing and Health Professions | 5 | 8% |
| Computer Science | 5 | 8% |
| Business, Management and Accounting | 3 | 5% |
| Other | 10 | 16% |
| Unknown | 21 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 02 April 2023.
All research outputs
#5,858,268
of 23,509,253 outputs
Outputs from Frontiers in Psychology
#8,473
of 31,334 outputs
Outputs of similar age
#90,481
of 317,488 outputs
Outputs of similar age from Frontiers in Psychology
#222
of 578 outputs
Altmetric has tracked 23,509,253 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 31,334 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.6. This one has gotten more attention than average, scoring higher than 72% 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 317,488 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 71% of its contemporaries.
We're also able to compare this research output to 578 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 61% of its contemporaries.