| Title |
Usage of a Responsible Gambling Tool: A Descriptive Analysis and Latent Class Analysis of User Behavior
|
|---|---|
| Published in |
Journal of Gambling Studies, January 2016
|
| DOI | 10.1007/s10899-015-9590-6 |
| Pubmed ID | |
| Authors |
David Forsström, Hugo Hesser, Per Carlbring |
| Abstract |
Gambling is a common pastime around the world. Most gamblers can engage in gambling activities without negative consequences, but some run the risk of developing an excessive gambling pattern. Excessive gambling has severe negative economic and psychological consequences, which makes the development of responsible gambling strategies vital to protecting individuals from these risks. One such strategy is responsible gambling (RG) tools. These tools track an individual's gambling history and supplies personalized feedback and might be one way to decrease excessive gambling behavior. However, research is lacking in this area and little is known about the usage of these tools. The aim of this article is to describe user behavior and to investigate if there are different subclasses of users by conducting a latent class analysis. The user behaviour of 9528 online gamblers who voluntarily used a RG tool was analysed. Number of visits to the site, self-tests made, and advice used were the observed variables included in the latent class analysis. Descriptive statistics show that overall the functions of the tool had a high initial usage and a low repeated usage. Latent class analysis yielded five distinct classes of users: self-testers, multi-function users, advice users, site visitors, and non-users. Multinomial regression revealed that classes were associated with different risk levels of excessive gambling. The self-testers and multi-function users used the tool to a higher extent and were found to have a greater risk of excessive gambling than the other classes. |
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 | 1 | 20% |
| France | 1 | 20% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 2% |
| Unknown | 60 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 21% |
| Student > Ph. D. Student | 10 | 16% |
| Student > Doctoral Student | 5 | 8% |
| Student > Bachelor | 5 | 8% |
| Professor | 3 | 5% |
| Other | 10 | 16% |
| Unknown | 15 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 17 | 28% |
| Social Sciences | 7 | 11% |
| Medicine and Dentistry | 4 | 7% |
| Nursing and Health Professions | 3 | 5% |
| Business, Management and Accounting | 3 | 5% |
| Other | 7 | 11% |
| Unknown | 20 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 17 September 2016.
All research outputs
#3,273,303
of 25,374,917 outputs
Outputs from Journal of Gambling Studies
#170
of 989 outputs
Outputs of similar age
#53,385
of 400,971 outputs
Outputs of similar age from Journal of Gambling Studies
#1
of 11 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 989 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.4. This one has done well, scoring higher than 82% 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 400,971 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 11 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 90% of its contemporaries.