Evidence map›Paper›PMID 36465287›Full record

ArticleFrontiers in psychiatry2022

One size does not fit all. Should gambling loss limits be based on income?

Elias Langeland, Ingvild Faxvaag Johnsen, Kaja Kastrup Sømme, Arne Magnus Morken, Eilin Kristine Erevik, Eirin Kolberg, Jakob Jonsson, Rune Aune Mentzoni, Ståle Pallesen

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Elias LangelandDepartment of Psychosocial Science, University of Bergen, Bergen, Norway.
Ingvild Faxvaag JohnsenDepartment of Psychosocial Science, University of Bergen, Bergen, Norway.
Kaja Kastrup SømmeDepartment of Psychosocial Science, University of Bergen, Bergen, Norway.
Arne Magnus MorkenDepartment of Psychosocial Science, University of Bergen, Bergen, Norway.
Eilin Kristine ErevikDepartment of Psychosocial Science, University of Bergen, Bergen, Norway.
Eirin KolbergDepartment of Psychosocial Science, University of Bergen, Bergen, Norway.
Jakob JonssonDepartment of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden.
Rune Aune MentzoniDepartment of Psychosocial Science, University of Bergen, Bergen, Norway.
Ståle PallesenDepartment of Psychosocial Science, University of Bergen, Bergen, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Previous research has suggested empirically based gambling loss limits, with the goal of preventing gambling related harm in the population. However, there is a lack of studies relating gambling loss limits to individual factors such as income. The current study examines whether gambling loss limits should be income-specific. Materials and methods: The dataset was derived from three representative cross-sectional surveys of the Norwegian population and consisted of 14,630 gamblers. Four income groups, based on a quartile approximation, were formed. Gambling related harm was measured with the Problem Gambling Severity Index (PGSI), and precision-recall (PR) analyses were used to identify loss limits for the different income groups at two levels of gambling severity: moderate-risk gambling and problem gambling. Results: For both levels of gambling severity, we found the lowest income group to have the lowest gambling loss limits, and the highest income group to have the highest loss limits, which compared to the loss limits for the total sample, were lower and higher, respectively. Calculating the cut-offs for moderate-risk gamblers, we found a consistently ascending pattern from the lowest to the highest income group. Calculating the cut-offs for problem gamblers, we found a similar pattern except for the two middle income groups. Conclusion: The results suggest that income moderates empirically derived gambling loss limits. Although replication is required, income-based gambling loss limits may have higher applied value for preventing gambling related harm, compared to general loss limits aimed at the entire population.

Indexed as

gambling loss limitsgambling related harmincome-specific loss limitslow-risk gambling limitsPGSIprecision-recallreceiver operating characteristicresponsible gambling

Identifiers

PMID36465287
PMCPMC9709812

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.