Evidence map›Paper›PMID 38358444›Full record

ArticleJournal of gambling studies2024

Using the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) to Predict Gambling Disorder Among U.S. College Students.

Fares Qeadan, Jamie Egbert, Benjamin Tingey, Abigail Plum, Tatiana Pasewark

Abstract read
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In one paragraph

Article in Journal of gambling studies, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.6field-weighted citation impact, top 34% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Fares QeadanParkinson School of Health Sciences and Public Health, Loyola University Chicago, 2160 N 1st Ave, Maywood, IL, 60153, USA. fqeadan@luc.edu.ORCID http://orcid.org/0000-0002-3376-220X
Jamie EgbertParkinson School of Health Sciences and Public Health, Loyola University Chicago, 2160 N 1st Ave, Maywood, IL, 60153, USA.
Benjamin TingeyParkinson School of Health Sciences and Public Health, Loyola University Chicago, 2160 N 1st Ave, Maywood, IL, 60153, USA.
Abigail PlumParkinson School of Health Sciences and Public Health, Loyola University Chicago, 2160 N 1st Ave, Maywood, IL, 60153, USA.
Tatiana PasewarkParkinson School of Health Sciences and Public Health, Loyola University Chicago, 2160 N 1st Ave, Maywood, IL, 60153, USA.
Loyola University Chicago · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) screening tool has not previously been used to evaluate risk for gambling disorder (GD). We aimed to assess the level at which each specific substance involvement score (SSIS), measured by ASSIST, most optimally predicted GD among U.S. college students. Data were analyzed for 141,769 students from the National College Health Assessment (fall 2019-spring 2021) utilizing multivariable logistic regression models. Sensitivities and specificities were utilized to find optimal cutoffs that best identified those with GD, overall and by biological sex and age group. Lower threshold of substance risk related to prescription opioids, cocaine, and hallucinogens (all with SSIS cutoffs of 4) predicts gambling disorder compared to sedatives (SSIS cutoff of 19). Younger students had lower thresholds of substance risk predicting GD than older students for heroin, but for all other substance classifications students 25 years and older had lower thresholds of SSIS predicting GD than students 18-24 years old. This study aids in the understanding that substance use behavior may put students at risk for other addictive behaviors such as GD. This study is the first to utilize the ASSIST tool to predict GD among U.S. college students, extending its application beyond substance use disorders. The identification of optimal cutoffs for each SSIS provides a novel approach to concurrently screen for GD and substance use disorders. This unique contribution could enhance early detection and intervention strategies for GD in the college student population.

Indexed as

GamblingStudentsSubstance-Related DisordersAdolescentAdultFemaleHumansMaleMass ScreeningSmokingUnited StatesUniversitiesYoung AdultASSISTCollege studentsGambling disorderOptimal cutoffSSISYouden’s index

Identifiers

PMID38358444
OpenAlexW4391843782

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.