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. 2026 Oct;23(5):375–388. doi: 10.36131/cnfioritieditore20260501

Gambling Involvement And Gambling-Related Problem Severity Among Psychiatric Outpatients And Community Participants

Antonino Costanzo 1,✉, Giuseppe Maniaci 2, Carmen Concerto 3, Antonio Bruno 5,6, Ugo Pace 1, Alessia Passanisi 1, Caterina La Cascia 2, Laura Ferraro 2, Simone Russo Coco 2, Rossana Geraci 1, Alessandro Rodolico 4, Antonio Trovato 3, Antonino Petralia 3, Maria Salvina Signorelli 3,7, William Torregrossa 5, Fabrizio Turiaco 5, Maria Rosaria Anna Muscatello 5,6, Adriano Schimmenti 1
PMCID: PMC13684849  PMID: 42909926

Abstract

Objective:

Gambling-related problems frequently co-occur with psychiatric conditions. This cross-sectional study examined past-year gambling involvement and gambling-related problem severity in psychiatric outpatients and community participants, with particular attention to the role of emotion dysregulation and impulsivity.

Method:

Participants were 815 adults, including 340 psychiatric outpatients and 475 community participants reporting no current psychiatric diagnosis. They completed measures of sociodemographic characteristics and past-year gambling. Gambling-related problem severity was assessed among participants who had gambled in the previous 12 months, whereas emotion dysregulation and impulsivity were assessed among psychiatric outpatients and among community participants who had gambled in the previous 12 months. Analyses examined whether psychiatric status was associated with past-year gambling involvement in the full sample and with gambling-related problem severity among participants who had gambled in the previous 12 months.

Results:

Psychiatric outpatients were more likely than community participants to report past-year gambling. In the full sample, gambling involvement was associated with younger age, male gender, and psychiatric status. Among past-year gamblers, psychiatric status was not independently associated with gambling-related problem severity. Higher motor impulsivity was associated with greater severity in the overall gambler sample, whereas emotion dysregulation showed no consistent associations across analyses.

Conclusions:

Gambling involvement and gambling-related problem severity showed partly distinct correlates: psychiatric status was associated with past-year gambling involvement but not independently with gambling-related problem severity among past-year gamblers. These findings support routine screening for gambling involvement and related harms in psychiatric outpatient services.

Introduction

Gambling is increasingly recognized as a public health issue: the World Health Organization (WHO, 2024) calls for regulation, monitoring and targeted interventions, and the Lancet Public Health Commission (Wardle et al., 2024) has framed gambling as a “health-harming” activity characterized by measurable health, social and economic harms, shifting attention from individuals to the gambling supply system and market conditions that amplify harm. Recent estimates indicate that 46.2% of adults and 17.9% of adolescents worldwide gambled in the past year; among adults, about 8.7% fall into “at-risk” categories and about 1.4% meet criteria for problematic gambling (PG; Tran et al., 2024). Despite this widespread involvement, help-seeking remains rare: a meta-analysis shows that only 1 in 25 moderate-risk gamblers and 1 in 5 problem gamblers seek help, reflecting barriers to access, stigma and low perceived need for treatment (Bijker et al., 2022).

Gambling lies on a continuum from non-problematic involvement to Gambling Disorder (GD), the clinical extreme of problematic gambling (PG), reflecting increasing risk and negative outcomes across social, occupational, interpersonal and health domains (Delfabbro, 2013; Holtgraves, 2009; Johansson et al., 2009; Raylu & Oei, 2002, 2004). In the revised fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5-TR; American Psychiatric Association [APA], 2022), GD is defined as an addictive disorder marked by loss of control, growing salience and persistence despite negative consequences. Complementing this diagnostic definition, a symptom-based perspective has conceptualized excessive gambling through core addictive features shared across behavioral and substance-related addictions, namely salience, mood modification, tolerance, withdrawal, conflict, and relapse (Griffiths, 2005). However, recent debates caution against relying exclusively on symptom checklists and encourage integrating symptom descriptions with process-oriented accounts of vulnerability and reinforcement (Billieux et al., 2015). In line with these integrative approaches, models of behavioral addiction also emphasize a compensatory view, conceptualizing potentially addictive activities as external regulators of negative internal states and means of bridging personal vulnerabilities. From this perspective, gambling can respond to unmet motivational needs (Schimmenti, 2023) and function as an emotion-regulation and escapism strategy to manage or avoid intense emotional states (Alaba-Ekpo et al., 2025; Neophytou et al., 2023). When reliance on these strategies becomes rigid, repetitive and prioritized over alternatives, involvement tends to crystallize into problematic patterns, with possible progression to clinical presentations (Santoro et al., 2025).

Epidemiological and clinical evidence indicates higher prevalence of PG in clinical than in general populations. A meta-analysis in psychotic disorders reports markedly higher prevalence than in controls (Corbeil et al., 2024). Prospective evidence from first-episode psychosis also indicates clinically relevant rates of problem gambling and identifiable predictors over time (Corbeil et al., 2026). National register studies from Sweden, Denmark, and Norway similarly document high psychiatric comorbidity, particularly mood and anxiety disorders, substantial co-occurrence between GD and Substance Use Disorders (around 22–23%), and greater criminal offending, health and social costs among people with GD compared with matched population samples. These studies also highlight gender-specific temporal patterns, with Substance Use Disorder tending to coincide with GD onset in men and more often following GD in women (Håkansson et al., 2018; Leino et al., 2023; Vestergaard et al., 2023). Accordingly, beyond diagnostic comorbidity, it is important to consider transdiagnostic psychological processes that may contribute to gambling involvement and gambling-related problem severity across psychiatric outpatients and community groups.

Emotion dysregulation and impulsivity are widely recognized as transdiagnostic risk and maintenance factors for multiple psychopathologies and potentially addictive behaviors (Caretta et al., 2018; Di Blasi et al., 2019; Maganuco et al., 2019; Rogier et al., 2020; Russo et al., 2022). Emotion dysregulation refers to maladaptive processing of internal or external stimuli, often manifesting as hyperarousal, mood instability and reactions that may be excessive, socially inappropriate or harmful to one’s interests (Ambrosini et al., 2013; Shaw et al., 2014; Tonacci et al., 2019). Empirical work documents robust associations between emotion dysregulation and PG (Piccoli et al., 2020; Rogier & Velotti, 2018; Rogier et al., 2020). Components most consistently linked to gambling severity include difficulty managing impulses, maintaining goals under emotional activation and deploying adequate regulatory strategies (Aldao et al., 2010; Maniaci et al., 2017; Velotti et al., 2021). Impulsivity, defined as a tendency to respond rapidly and without planning to internal or external stimuli without considering possible negative consequences (Raji et al., 2025), also plays an important role in the development and maintenance of PG (Gori et al., 2022; Rogier, Colombi, et al., 2022b; Schluter & Hodgins, 2021; Topino et al., 2024). Motor impulsivity and non-planning are consistently associated with indices of severity and problematicity (López-Torres et al., 2021), and among more problematic gamblers, difficulties in response inhibition, risky decision-making and steeper delay discounting are also observed (Ioannidis et al., 2019). Although closely related in many clinical presentations, emotion dysregulation and impulsivity are conceptually distinct constructs (Schreiber et al., 2012; Garofalo et al., 2018; Lopes & Tavares, 2025; Weiss et al., 2015). Within gambling research, the Pathways Model offers a more specific framework for understanding this distinction. In the original model, the emotionally vulnerable pathway emphasizes affective vulnerability and gambling as a maladaptive coping strategy, whereas the antisocial-impulsivist pathway highlights impulsivity, poor inhibitory control, and broader externalizing features (Blaszczynski & Nower, 2002). The revised model underscores stress-coping motives, impulsive and risk-taking traits in more severe gambling trajectories (Nower et al., 2022). Accordingly, the Pathways Model provides a useful theoretical framework for interpreting how emotion dysregulation and impulsivity may contribute in partly distinct ways to gambling involvement and to the escalation of gambling-related problems (Blaszczynski & Nower, 2002; Nower et al., 2022). The Pathways Model may be informative not only for clinically severe gambling patterns, but also for understanding variation across non-clinical forms of gambling involvement (Billieux et al., 2022; Clark et al., 2022).

Given this background, the present study examined past-year gambling involvement and gambling-related problem severity in psychiatric outpatients and community participants. The first primary analysis tested the association between psychiatric status and past-year gambling involvement in the full sample. The second primary analysis examined psychiatric status, emotion dysregulation, and impulsivity in relation to gambling-related problem severity among participants who had gambled in the previous 12 months. Secondary exploratory analyses examined gambling involvement within psychiatric outpatients and community participants and examined emotion dysregulation and impulsivity in relation to gambling involvement and gambling-related problem severity within the psychiatric outpatient group.

Methods

Participants and procedures

A total of 993 participants were initially recruited across the two study arms. Of these, 815 were retained in the final analytic sample (table 1), including 340 psychiatric outpatients (41.7%) and 475 community participants (58.3%). In the psychiatric arm, 518 outpatients were initially recruited, and 340 were retained in the analytic sample after exclusion of 178 incomplete questionnaires. In the community arm, the analytic sample consisted of 475 eligible community respondents who completed the online survey. Overall, 235 participants (28.8%) had gambled at least once in the past 12 months and were classified as gamblers, whereas 580 (71.2%) were classified as non-gamblers. Cross-classifying psychiatric status and past-year gambling involvement yielded four groups: 174 psychiatric outpatient gamblers, 61 community gamblers, 166 psychiatric outpatient non-gamblers, and 414 community non-gamblers (table 2).

Table 1.

Sociodemographic and clinical characteristics of the total sample, psychiatric outpatients, and community participants

Total sample
(n = 815)		 Psychiatric
outpatients (n = 340)		 Community
participants (n = 475)
Age 39.39 (14.74) 45.96 (14.20) 34.69 (13.26)
Years of education 13.33 (3.75) 11.60 (3.53) 14.57 (3.39)
Gender Men 352 (43.2) 148 (43.5) 204 (42.9)
Women 463 (56.8) 192 (56.5) 271 (57.1)
Nationality Italian 805 (98.8) 336 (98.8) 469 (98.7)
Other 10 (1.2) 4 (1.2) 6 (1.3)
Marital status
(Married) 		Yes 272 (33.4) 116 (34.1) 156 (32.8)
No 543 (66.6) 224 (65.9) 319 (67.2)
Employment
(Working/studying) 		Yes 566 (69.4) 169 (49.7) 397 (83.6)
No 249 (30.6) 171 (50.3) 78 (16.4)
Living alone Yes 97 (11.9) 53 (15.6) 44 (9.3)
No 718 (88.1) 287 (84.4) 431 (90.7)
Family history of psychiatric
disorder 		Yes 352 (43.2) 175 (51.5) 177 (37.3)
No 463 (56.8) 165 (48.5) 298 (62.7)
Family history of problematic
gambling 		Yes 104 (12.8) 54 (15.9) 50 (10.5)
No 711 (87.2) 286 (84.1) 425 (89.5)
Substance use (last 30 days) Yes 182 (22.3) 158 (46.5) 24 (5.1)
No 633 (77.7) 182 (53.5) 451 (94.9)
Alcohol use (last 30
days) 		Yes 416 (51.0) 96 (28.2) 320 (67.4)
No 399 (49.0) 244 (71.8) 155 (32.6)
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Note. Continuous variables are reported as M (SD); categorical variables as n (%). Percentages are calculated within columns.

Table 2.

Sociodemographic and clinical characteristics by gambling involvement and recruitment group

Gamblers
(n = 235)		 Non-
gamblers (n =
580)		 Psychiatric
outpatient
gamblers
(n = 174)		 Community
gamblers
(n = 61)		 Psychiatric
outpatient
non-gamblers
(n = 166)		 Community
non-gamblers
(n = 414)
Age 39.19 (14.63) 39.48 (14.79) 43.63 (14.07) 26.52 (6.66) 48.40 (13.97) 35.90 (13.57)
Years of education 12.39 (3.64) 13.71 (3.72) 11.55 (3.50) 14.79 (2.92) 11.65 (3.57) 14.54 (3.46)
Gender Men 143 (60.9) 209 (36.0) 92 (52.9) 51 (83.6) 56 (33.7) 153 (37.0)
Women 92 (39.1) 371 (64.0) 82 (47.1) 10 (16.4) 110 (66.3) 261 (63.0)
Nationality Italian 235 (100) 570 (98.3) 174 (100) 61 (100) 162 (97.6) 408 (98.6)
Other 0 (0.0) 10 (1.7) 0 (0.0) 0 (0.0) 4 (2.4) 6 (1.4)
Marital status
(Married) 		Yes 50 (21.3) 222 (38.3) 45 (25.9) 5 (8.2) 71 (42.8) 151 (36.5)
No 185 (78.7) 358 (61.7) 129 (74.1) 56 (91.8) 95 (57.2) 263 (63.5)
Employment
(Working/studying) 		Yes 147 (62.6) 419 (72.2) 91 (52.3) 56 (91.8) 78 (47.0) 341 (82.4)
No 88 (37.4) 161 (27.8) 83 (47.7) 5 (8.2) 88 (53.0) 73 (17.6)
Living alone Yes 36 (15.3) 61 (10.5) 29 (16.7) 7 (11.5) 24 (14.5) 37 (8.9)
No 199 (84.7) 519 (89.5) 145 (83.3) 54 (88.5) 142 (85.5) 377 (91.1)
Family history of
psychiatric disorder 		Yes 99 (42.1) 253 (43.6) 83 (47.7) 16 (26.2) 92 (55.4) 161 (38.9)
No 136 (57.9) 327 (56.4) 91 (52.3) 45 (73.8) 74 (44.6) 253 (61.1)
Family history of
problematic gambling 		Yes 37 (15.7) 67 (11.6) 29 (16.7) 8 (13.1) 25 (15.1) 42 (10.1)
No 198 (84.3) 513 (88.4) 145 (83.3) 53 (86.9) 141 (84.9) 372 (89.9)
Substance use (last 30
days) 		Yes 100 (42.6) 82 (14.1) 93 (53.4) 7 (11.5) 65 (39.2) 17 (4.1)
No 135 (57.4) 498 (85.9) 81 (46.6) 54 (88.5) 101 (60.8) 397 (95.9)
Alcohol use (last 30
days) 		Yes 111 (47.2) 305 (52.6) 56 (32.2) 55 (90.2) 40 (24.1) 265 (64.0)
No 124 (52.8) 275 (47.4) 118 (67.8) 6 (9.8) 126 (75.9) 149 (36.0)
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Note. Continuous variables are reported as M (SD); categorical variables as n (%). Percentages are calculated within columns.

The study was conducted in accordance with Regulation (EU) 2016/679 (General Data Protection Regulation [GDPR]) and Italian Law No. 219 of 22 December 2017. Ethical approval was granted by the Ethics Committee for Clinical Experimentation of the University Hospital of Catania (Resolution No. 922, 17 April 2024), the Local Ethics Committee 1 of the University of Palermo (Minute No. 13, 22 May 2024), the Local Ethics Committee of the University of Messina (Protocol No. 62-2024; Minutes No. 1, 21 January 2025), and the Ethics Committee of Kore University of Enna (Protocol No. 15288/2024, 17 July 2024). All participants were at least 18 years old and provided informed consent.

Psychiatric outpatients were recruited between June 2024 and December 2025 from the outpatient psychiatric services of three collaborating university hospitals located in Catania, Palermo, and Messina, Italy. Recruitment followed a consecutive sampling approach, including patients seeking outpatient psychiatric evaluation during the study period. Inclusion criteria were age between 18 and 65 years, current outpatient psychiatric care, ability to provide informed consent, and either a DSM-5-TR psychiatric diagnosis in one of the following categories: schizophrenia/psychotic spectrum disorder, bipolar disorder, major depressive disorder, Cluster B personality disorder, anxiety disorder, obsessive-compulsive disorder, eating disorder, or ongoing diagnostic assessment within the same outpatient services. Exclusion criteria were the presence of organic brain disease, a documented diagnosis of intellectual disability or neurodevelopmental disorder, dementia, and acute psychiatric symptomatology at the time of assessment. Psychiatric diagnoses were established by treating psychiatrists in routine clinical practice according to DSM-5-TR criteria (APA, 2022). At the time of assessment, psychiatric outpatients were receiving routine outpatient psychiatric care, including psychopharmacological treatment when clinically indicated. No participant had a current clinical diagnosis of GD recorded in routine outpatient psychiatric assessment; therefore, no participant was excluded on that basis. Participants were not systematically assessed for GD through a structured DSM-5-TR diagnostic interview. Accordingly, the absence of a recorded GD diagnosis should not be interpreted as absence of gambling-related harms or problem-gambling severity. The paper-and-pencil questionnaire was self-administered. Because complete item-level data were required to compute total and subscale scores, incomplete questionnaires were excluded prior to analysis. Missing data were not imputed because they were confined to the paper-and-pencil psychiatric sample, whereas online responses were mandatory. Within the retained psychiatric outpatient sample, 292 participants (85.9%) had one recorded diagnosis, 9 (2.6%) had two co-occurring diagnoses, and 39 (11.5%) were undergoing diagnostic assessment. The diagnostic composition of the psychiatric outpatient sample, separately for past-year gamblers and non-gamblers, is reported in table 3. Diagnostic categories were not mutually exclusive. Recent substance use was assessed as a self-reported 30-day behavior and was not treated as a diagnosis of substance use disorder.

Table 3.

Diagnostic composition of psychiatric outpatients by past-year gambling involvement

Full psychiatric cohort
(n = 340)		 Psychiatric outpatient
gamblers
(n = 174)		 Psychiatric outpatient
non-gamblers
(n = 166)
Bipolar disorder spectrum 44 (12.9) 28 (16.1) 16 (9.6)
Schizophrenia/psychotic spectrum 49 (14.4) 29 (16.7) 20 (12.0)
Major depressive disorder 92 (27.1) 44 (25.3) 48 (28.9)
Cluster B personality disorder 44 (12.9) 25 (14.4) 19 (11.4)
Anxiety disorder 52 (15.3) 23 (13.2) 29 (17.5)
Obsessive-compulsive disorder 27 (7.9) 15 (8.6) 12 (7.2)
Eating disorder 2 (0.6) 0 (0.0) 2 (1.2)
Diagnostic assessment pending 39 (11.5) 15 (8.6) 24 (14.5)
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Note. Categorical variables are reported as n (%). Percentages are calculated within columns. Diagnostic categories are based on clinical diagnoses recorded in the psychiatric outpatient services. Categories are not mutually exclusive because 9 participants had two co-occurring diagnoses. Bipolar disorder spectrum includes bipolar I disorder, bipolar II disorder, and bipolar disorder not otherwise specified. Schizophrenia/psychotic spectrum includes schizophrenia, schizoaffective disorder, and psychosis not otherwise specified. The category “diagnostic assessment pending” refers to participants undergoing outpatient assessment at the time of data collection.

Community participants were recruited between January 2025 and December 2025 through an anonymous online survey disseminated via social media. Eligibility criteria were age 18 years or older, provision of informed consent, and self-reported absence of a current psychiatric diagnosis. No clinical diagnostic interview was administered in the community arm. The community group was not individually matched to the psychiatric outpatient group. Accordingly, between-group comparisons should be interpreted as naturalistic adjusted comparisons rather than as matched contrasts. To account for observed between-group differences, sociodemographic and clinical covariates were included in the regression models.

No a priori power analysis was conducted. Sample size was determined by the number of eligible psychiatric outpatients consecutively recruited during the study period and by the number of eligible community respondents who completed the online survey during the same period.

All items presented in the online survey were mandatory; therefore, the online dataset contained no missing responses on administered items. Across both study arms, the assessment included age, gender, nationality, years of education, marital status, employment status, living arrangement, family history of psychiatric disorder, family history of problematic gambling, recent alcohol and substance use, and past-year gambling involvement. Race and ethnicity were not assessed in either study arm and therefore were not included in the analyses. Within the community group, participants who reported no gambling in the past 12 months did not complete the measures of gambling severity, impulsivity, or emotion dysregulation, whereas those who had gambled at least once in the past 12 months completed the measures of gambling severity, impulsivity, and emotion dysregulation.

Measures

The Canadian Problem Gambling Index (CPGI; Ferris & Wynne, 2001; Italian translation by Capitanucci, 2006) is a 9-item self-report measure of gambling-related problem severity over the past 12 months. Items are rated on a 4-point Likert scale from 0 (never) to 3 (almost always) and summed to yield a total severity score. Consistent with standard CPGI scoring, total scores can be used to classify respondents as non-problem gamblers (0), low-risk gamblers (1–2), moderate-risk gamblers (3–7), or problem gamblers (8 or higher). The CPGI has shown good psychometric properties, including in Italian samples (Colasante et al., 2013). In the present study, internal consistency for the CPGI total score was excellent in the full gambler sample and the psychiatric outpatient gambler subgroup (Cronbach’s α = .93 in both subsamples; see table 4).

Table 4.

Internal consistency coefficients (Cronbach’s \alpha) for measures used in the regression models

Measure Gamblers
(n = 235)		 Psychiatric
outpatients
(n = 340)		 Psychiatric
outpatient gamblers
(n = 174)
CPGI total .93 — .93
DERS – Nonacceptance of emotional responses .85 .83 .83
DERS – Difficulties engaging in goal-directed behavior .73 .70 .67
DERS – Lack of emotional awareness .76 .69 .72
DERS – Impulse control difficulties .81 .78 .79
DERS – Limited access to emotion regulation strategies .87 .84 .84
DERS – Lack of emotional clarity .66 .54 .60
BIS-11 – Attentional impulsivity .59 .50 .53
BIS-11 – Motor impulsivity .66 .63 .60
BIS-11 – Non-planning impulsivity .69 .62 .64
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Note. CPGI = Canadian Problem Gambling Index; DERS = Difficulties in Emotion Regulation Scale; BIS-11 = Barratt Impulsiveness Scale-11. Dashes indicate that the measure was not used in that analytic subsample.

The Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, 2004; Italian translation by Giromini et al., 2012) is a 36-item self-report measure of emotion dysregulation. Items are rated on a 5-point Likert scale from 1 (almost never) to 5 (almost always), with higher scores indicating greater difficulties. The scale yields a total score and six subscales: nonacceptance of emotional responses, difficulties engaging in goal-directed behavior, impulse control difficulties, lack of emotional awareness, limited access to emotion regulation strategies, and lack of emotional clarity. The Italian version has shown good psychometric properties (Giromini et al., 2012). In the present study, internal consistency coefficients for the DERS subscales used in the regression models ranged from .54 to .87 across subsamples (see table 4).

The Barratt Impulsiveness Scale–11 (BIS-11; Patton et al., 1995; Italian translation by Fossati et al., 2001) is a 30-item self-report measure of trait impulsivity. Items are rated on a 4-point Likert scale from 1 (rarely/never) to 4 (almost always/always), with higher scores indicating greater impulsivity. The BIS-11 yields a total score, six first-order factors (attention, motor, self-control, cognitive complexity, perseverance and cognitive instability) and three second-order factors (attentional, motor and non-planning impulsivity). The Italian adaptation has shown good psychometric properties (Fossati et al., 2001). In the present study, internal consistency coefficients for the BIS-11 subscales used in the regression models ranged from .50 to .69 across analytic subsamples (see table 4).

Statistical analysis

Statistical analyses were conducted using SPSS software (IBM Corp., 2016). Descriptive statistics were computed for the total sample and for subgroups defined by psychiatric status and past-year gambling involvement (tables 1–3). Primary analyses tested whether psychiatric status was associated with past-year gambling involvement in the full sample and whether psychiatric status, emotion dysregulation, and impulsivity were associated with gambling-related problem severity among past-year gamblers. Secondary exploratory analyses consisted of two within-group logistic regressions predicting past-year gambling involvement, one among psychiatric outpatients and one among community participants; a logistic regression predicting past-year gambling involvement among psychiatric outpatients from age, years of education, gender, and DERS and BIS-11 subscales; and a multiple linear regression predicting CPGI total score among psychiatric outpatient gamblers (tables 5–7). Logistic regression models were used for categorical outcomes and multiple linear regression models for gambling severity (CPGI total score).

Table 5.

Logistic regression models with sociodemographic and clinical predictors of gambling status (gambler vs. non-gambler) in the full sample and within psychiatric outpatients and community participants

Full sample
(N = 815)
Outcome: Gambling status		 Psychiatric outpatients
(N = 340)
Outcome: Gambling status		 Community participants
(N = 475)
Outcome: Gambling status
B SE OR [95% CI] p B SE OR [95% CI] p B SE OR [95% CI] p
Age -.03 .01 .97 [.95-.98] <.01 -.02 .01 .98 [.96-1.00] .04 -.10 .03 .90 [.86-.95] <.01
Years of education -.02 .03 .98 [.92-1.03] .41 -.03 .04 .97 [.90-1.04] .34 .03 .06 1.03 [.91-1.15] .67
Gender -1.19 .20 .30 [.21-.45] <.01 -.71 .24 .49 [0.31-.78] <.01 -2.16 .39 .12 [.05-.25] <.01
Psychiatric status 2.60 .29 13.45 [7.59-23.84] <.01
Marital status -.61 .25 .55 [.33-.89] .02 -.56 .29 .57 [.33-1.01] .06 -.28 .61 .76 [.23-2.49] .65
Employment -.23 .23 .80 [.51-1.26] .33 -.07 .25 .93 [.57-1.53] .79 -.59 .62 .56 [.17-1.86] .34
Living arrangement -.16 .29 .85 [.48-1.52] .58 -.03 .35 .97 [.48-1.94] .92 -.60 .54 .55 [.19-1.59] .27
Family history of psychiatric disorder -.41 .19 .66 [.45-.97] .03 -.30 .23 .74 [.47-1.17] .20 -.58 .35 .56 [.28-1.12] .10
Family history of problematic gambling .22 .27 1.25 [.73-2.11] .41 .11 .32 1.11 [.59-2.08] .74 .39 .49 1.48 [.57-3.90] .42
Substance use in last 30 days .15 .24 1.17 [.73-1.85] .51 .28 .26 1.33 [.80-2.22] .28 .58 .55 1.78 [.61-5.22] .29
Alcohol use in last 30 days .31 .23 1.37 [.87-2.15] .18 -.05 .29 .95 [.54-1.69] .87 1.07 .48 2.93 [1.15-7.47] .02
Omnibus χ2 (df, p) 245.72 (11), p < .01 31.12 (10), p < .01 103.41 (10), p < .01
Nagelkerke R2 .37 .12 .37
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Note. B = unstandardized logistic regression coefficient; OR = odds ratio; 95% CI = 95% confidence interval. Reference categories were non-gambler, male, not married, not working/studying, living alone, no family history of psychiatric disorder or problematic gambling, no substance use, no alcohol use. Gender is coded 1 = male and 2 = female in the dataset; coefficients represent category 2 compared with 1. Gambling status: 1 = gambler, 0 = non-gambler. In the full-sample model, psychiatric status was coded 1 = psychiatric outpatient and 0 = community participant. The full-sample model constitutes a primary analysis; the models within psychiatric and community groups are secondary exploratory analyses. Exploratory p values were additionally evaluated using the Benjamini–Hochberg false discovery rate procedure (see Supplementary Table S3).

Table 7.

Linear regression models predicting gambling severity (CPGI total score) in all gamblers and psychiatric outpatient gamblers

All gamblers
(N = 235)
Outcome: CPGI total score 		Psychiatric outpatient gamblers
(N = 174)
Outcome: CPGI total score
B SE β p B SE β p
Age .04 .03 .11 .16 .06 .03 .15 .06
Years of education -.06 .11 -.04 .54 -.11 .13 -.07 .38
Gender -1.98 .73 -.18 <.01 -1.67 .85 -.15 .05
Psychiatric status -1.68 1.00 -.14 .10
DERS – Nonacceptance of emotional responses .00 .08 .00 .98 -.03 .09 -.04 .69
DERS – Difficulties engaging in goal-directed behavior .01 .10 .01 .91 -.05 .12 -.04 .68
DERS – Lack of emotional awareness .07 .07 .07 .31 .04 .08 .04 .64
DERS – Impulse control difficulties -.20 .09 -.23 .02 -.24 .10 -.27 .02
DERS – Limited access to emotion regulation strategies .10 .07 .16 .16 .08 .09 .12 .34
DERS – Lack of emotional clarity -.07 .11 -.06 .49 -.07 .12 -.06 .58
BIS-11 – Attentional impulsivity .01 .11 .00 .96 .09 .14 .06 .51
BIS-11 – Motor impulsivity .28 .09 .26 <.01 .29 .11 .25 .01
BIS-11 – Non-planning impulsivity .13 .09 .13 .13 .11 .10 .10 .30
R2 .14 .15
Adjusted R2 .09 .08
F(df1, df2), p 2.86 (13, 221)' p <.01 2.31 (12, 161)' p =.01
Durbin–Watson 2.14 2.19
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Note. CPGI = Canadian Problem Gambling Index. DERS = Difficulties in Emotion Regulation Scale; BIS-11 = Barratt Impulsiveness Scale-11. BIS-11 subscales: attentional impulsivity, motor impulsivity, and non-planning impulsivity. B = unstandardized regression coefficient; β = standardized coefficient. Gender coded 1 = male and 2 = female in the dataset; coefficients represent category 2 compared with 1. Psychiatric status is only included as a predictor in the model with all gamblers. The model including all gamblers constitutes a primary analysis; the psychiatric outpatient gamblers model is a secondary exploratory analysis. Exploratory p values were additionally evaluated using the Benjamini–Hochberg false discovery rate procedure (see Supplementary table S3).

Diagnosis-specific comparisons were restricted to the 301 psychiatric outpatients with one or more recorded diagnoses; the 39 participants undergoing diagnostic assessment were excluded from these comparisons. Within this subsample, exploratory comparisons of past-year gambling involvement were conducted separately for each broad recorded diagnostic category, coded as present versus absent. Because diagnostic categories were not mutually exclusive, no omnibus diagnosis-by-gambling test was performed. Pearson chi-square tests were used for bipolar spectrum disorder, schizophrenia/psychotic spectrum disorder, major depressive disorder, Cluster B personality disorder, anxiety disorder, and obsessive-compulsive disorder; Fisher’s exact test was used for eating disorder because of sparse cells. Benjamini–Hochberg false discovery rate correction was applied across the seven diagnosis-specific tests.

For the secondary exploratory regression analyses, p values were additionally evaluated using the Benjamini–Hochberg false discovery rate procedure across the 44 predictor-level tests from the two within-group logistic models predicting past-year gambling involvement, the logistic regression among psychiatric outpatients including age, years of education, gender, and DERS and BIS-11 subscales, and the linear regression among psychiatric outpatient gamblers predicting CPGI total score. As a sensitivity analysis for the primary full-sample comparison, propensity scores for psychiatric status were estimated using age, years of education, gender, marital status, employment, living arrangement, family history of psychiatric disorder, family history of problematic gambling, recent substance use, and recent alcohol use. Overlap weights were then computed to emphasize participants with common support across psychiatric outpatient and community participants, and weighted logistic regression models were used to re-estimate the association between psychiatric status and past-year gambling involvement. This sensitivity analysis was restricted to the full-sample primary comparison.

Results

Results are presented according to the distinction between primary and secondary exploratory analyses. Primary analyses addressed the two main study questions concerning past-year gambling involvement in the full sample and gambling-related problem severity among past-year gamblers. Secondary exploratory analyses examined complementary patterns within specific subsamples.

Primary analyses

Past-year gambling involvement in the full sample

In the full sample (N = 815), a binary logistic regression model examined sociodemographic and clinical predictors of gambling status (non-gambler vs. gambler; table 5). Covariate balance before and after overlap weighting is reported in Supplementary table S1. An overlap-weighted sensitivity analysis examined robustness to between-group imbalance (Supplementary table S2).

Gambling-related problem severity among gamblers

Of the 235 participants who had gambled in the previous 12 months, 101 (43.0%) were classified as non-problem gamblers, 60 (25.5%) as low-risk gamblers, 37 (15.7%) as moderate-risk gamblers, and 37 (15.7%) as problem gamblers according to the CPGI (M = 3.26, SD = 5.41). A multiple linear regression model examined sociodemographic variables, psychiatric status, emotion dysregulation, and impulsivity as correlates of gambling-related problem severity (CPGI total score; table 7).

Secondary exploratory analyses

Past-year gambling involvement in psychiatric outpatients

In the psychiatric outpatient subsample (n = 340), an exploratory logistic regression model examined sociodemographic and clinical predictors of gambling status (table 5). A second model included age, years of education, gender, DERS subscales, and BIS-11 subscales as predictors of gambling involvement (table 6). Among the 301 psychiatric outpatients with one or more recorded diagnoses, exploratory diagnosis-specific comparisons showed no statistically significant association between any broad recorded diagnostic category and past-year gambling involvement. The largest unadjusted difference was observed for bipolar spectrum disorder, χ2(1) = 2.42, p = .12. The remaining Pearson chi-square tests were non-significant (all p ≥ .17), and Fisher’s exact test for eating disorder was non-significant (p = .22). No diagnosis-specific comparison remained significant after Benjamini–Hochberg correction (all qBH ≥ .44).

Table 6.

Logistic regression model predicting past-year gambling status from emotion dysregulation and impulsivity subscales in the psychiatric outpatient sample

Psychiatric outpatients (N = 340)
Outcome: Gambling status
B SE OR [95% CI] p
Age -.02 .01 .98 [.96-.99] <.01
Years of education -.02 .03 .98 [.92-1.05] .59
Gender -.79 .24 .45 [.28-.73] <.01
DERS – Nonacceptance of emotional responses -.04 .03 .96 [.91-1.01] .10
DERS – Difficulties engaging in goal-directed behavior .00 .03 1.00 [.94-1.07] .95
DERS – Lack of emotional awareness -.03 .02 .97 [.93-1.01] .15
DERS – Impulse control difficulties .04 .03 1.04 [.98-1.10] .23
DERS – Limited access to emotion regulation strategies .03 .03 1.03 [.98-1.08] .23
DERS – Lack of emotional clarity -.04 .03 .96 [.90-1.03] .22
BIS-11 – Attentional impulsivity -.07 .04 .94 [.87-1.01] .10
BIS-11 – Motor impulsivity .05 .03 1.05 [.99-1.11] .10
BIS-11 – Non-planning impulsivity .00 .03 1.00 [.95-1.05] .88
Omnibus χ2 (df, p) 37.14 (12)' p <.01
Nagelkerke R2 .14
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Note. DERS = Difficulties in Emotion Regulation Scale; BIS-11 = Barratt Impulsiveness Scale-11. B = unstandardized logistic regression coefficient; OR = odds ratio; 95% CI = 95% confidence interval. Gender is coded 1 = male and 2 = female; coefficients represent category 2 compared with 1. Gambling status: 1 = gambler, 0 = non-gambler. This is a secondary exploratory analysis; p values were additionally evaluated using the Benjamini–Hochberg false discovery rate procedure (see Supplementary Table S3).

Gambling-related problem severity in psychiatric outpatient gamblers

Among psychiatric outpatient gamblers (n = 174), an exploratory multiple linear regression model used CPGI total score as the outcome and sociodemographic variables, DERS subscales, and BIS-11 subscales as predictors (table 7).

Past-year gambling involvement in community participants

Among community participants (n = 475), an exploratory logistic regression model tested sociodemographic and clinical predictors of gambling status (table 5).

Nominally significant secondary exploratory associations that did not remain significant after Benjamini–Hochberg correction are reported in Supplementary table S3.

Discussion

The present study examined gambling involvement and gambling-related problem severity in psychiatric outpatients and community participants, with particular attention to emotion dysregulation and impulsivity. Two main findings emerged. First, psychiatric outpatients were more likely than community participants to report past-year gambling. Second, among individuals who had gambled in the past 12 months, psychiatric status was not independently associated with greater gambling-related problem severity.

These findings should be interpreted in light of current public-health approaches, which describe gambling as a common activity with harms distributed along a continuum and affecting many individuals below diagnostic thresholds (Tran et al., 2024; WHO, 2024; Wardle et al., 2024). Within this continuum, epidemiological and clinical evidence consistently shows elevated rates of gambling-related problems among people with mental disorders compared with the general population (Corbeil et al., 2024; Håkansson et al., 2018; Karlsson & Håkansson, 2018; Vestergaard et al., 2023). In this sample, psychiatric status was associated with a higher likelihood of past-year gambling involvement; the severity model accounted for a limited proportion of variance in CPGI scores (adjusted R2 = .09). Accordingly, the findings distinguish past-year gambling involvement from gambling-related problem severity but do not address diagnostic differences in GD per se (Corbeil et al., 2024; Vestergaard et al., 2023).

Gamblers were more often men, less likely to be married, and more likely to report recent substance use and, in the community group, were more likely to report recent alcohol use. This pattern is consistent with evidence linking male gender, younger age, being unmarried or separated, lower education, and substance use to greater risk of gambling-related problems in the general adult population (Allami et al., 2021; Johansson et al., 2009). In adjusted models, younger age, male gender, and psychiatric status showed the most consistent associations with gambling involvement in the full sample.

Overall, these findings suggest that gambling involvement in both psychiatric and community contexts is embedded in a broader constellation of sociodemographic and clinical vulnerabilities, including gender, relational status, and recent substance or alcohol use, with younger age also associated with gambling involvement in the adjusted model (Allami et al., 2021; Håkansson et al., 2018; Johansson et al., 2009). This pattern is broadly consistent with the Pathways Model of problem gambling, which provides a useful framework for interpreting how affective vulnerability and impulsivity-related externalizing features may be differentially involved in gambling-related risk, without implying that the present study directly identified specific gambling pathways (Blaszczynski & Nower, 2002; Nower et al., 2022).

Within the psychiatric outpatient subsample, exploratory diagnosis-specific comparisons did not identify a statistically significant association between any broad recorded diagnostic category and past-year gambling involvement after Benjamini–Hochberg correction. The psychiatric outpatient sample was diagnostically heterogeneous, as shown in table 3. These findings are compatible with perspectives that view gambling-related problems as potentially involving shared psychological mechanisms—such as emotion dysregulation and impulsivity—across diagnostic categories (Rogier & Velotti, 2018; Rogier et al., 2020; Velotti et al., 2021). They also align with evidence suggesting that GD can precede, follow, or co-occur with a range of other mental disorders in heterogeneous temporal patterns, rather than being reducible to a simple epiphenomenon of another primary condition (Leino et al., 2023; Palomäki et al., 2026; Vestergaard et al., 2023). Psychiatric status was not independently associated with gambling-related problem severity in the adjusted model. Psychiatric outpatient and community gamblers nevertheless differed in several sociodemographic and clinical characteristics: community gamblers were predominantly men, more often working or studying, and more likely to report recent alcohol use, whereas psychiatric outpatient gamblers were older, more often out of work, more likely to report recent substance use in unadjusted comparisons, and more likely to report a family history of psychiatric disorder. Taken together, these findings suggest that psychiatric outpatient and community gamblers may reflect partly distinct clinical profiles differing in broader psychosocial and clinical burden (Håkansson et al., 2018; Karlsson & Håkansson, 2018; Leino et al., 2023).

Emotion dysregulation and impulsivity have both been linked to gambling-related problems, with meta-analytic evidence supporting associations between emotion dysregulation and gambling disorder, as well as between impulsivity-related deficits and gambling severity (Ioannidis et al., 2019; Velotti et al., 2021).

Among gamblers, higher gambling severity was associated with male gender and higher BIS-11 motor impulsivity, whereas psychiatric status and most DERS subscales were not independently associated with severity. The only exception was a small inverse association with the DERS impulse subscale. Among psychiatric outpatient gamblers, higher BIS-11 motor impulsivity and lower DERS impulse control difficulties were nominally associated with greater gambling-related problem severity, but neither association remained significant after Benjamini–Hochberg correction. Given the modest size and counterintuitive direction of this effect, it may reflect complex shared variance with other impulsivity and emotion-dysregulation dimensions rather than a genuinely protective association, and it should therefore be interpreted cautiously. This pattern does not support a straightforward interpretation of emotion dysregulation as linearly associated with gambling-related problem severity in the present data. The relationship between emotion-regulation processes and gambling-related outcomes may be complex: strategies generally considered adaptive may, in some contexts, be associated with stronger gambling-related cognitive biases (Ruiz de Lara et al., 2019). In the present sample, the full model, including the assessed DERS and BIS-11 subscales, accounted for only a small portion of individual differences in gambling-related problem severity. Although prior research, particularly in clinical samples of individuals with gambling disorder, has linked emotion dysregulation to gambling-related difficulties (Velotti et al., 2021), the present data showed a more limited and inconsistent pattern. This discrepancy may reflect characteristics of the sample, the dimensional severity outcome assessed, or gambling-relevant processes not captured by the present measures. In the full gambler model, higher motor impulsivity was associated with greater gambling-related problem severity, in line with evidence highlighting the role of affect-driven and behavioral impulsivity in escalating gambling involvement and related problems (Ioannidis et al., 2019; López-Torres et al., 2021; Schluter & Hodgins, 2021).

Although associations with emotion dysregulation were more limited and less consistent than expected, they do not rule out process-oriented accounts of gambling, which conceptualize gambling as a strategy for regulating distressing internal states (Neophytou et al., 2023; Rogier, Capone, et al., 2022a). These accounts offer a possible framework in which gambling may function as a compensatory form of regulation in response to distress (Costanzo et al., 2024; Santoro et al., 2025; Schimmenti et al., 2025). Such accounts also allow for the possibility that gambling may provide a controllable niche of experience when emotional states are insufficiently processed, mentalized, or symbolized (Ciccarelli et al., 2021; Cosenza et al., 2019; Rogier & Velotti, 2018). However, these mechanisms were not measured and cannot be inferred from the present findings.

Additional clinical context is provided by the pattern of associations involving family history and substance use. In the total sample, and within both psychiatric outpatient and community groups, gamblers were more likely than non-gamblers to report recent substance use. These findings are consistent with evidence suggesting that familial exposure to gambling and other substance use and related problems may increase vulnerability to gambling-related difficulties through a combination of genetic liability, social learning, and the modelling of gambling as a way of coping with stress or emotional distress (Allami et al., 2021; Johansson et al., 2009). More broadly, they are compatible with the view that gambling may co-occur with substance use within a broader pattern of externally oriented attempts to regulate affective states and self-experience (Gori et al., 2022; Mestre-Bach et al., 2023).

Clinically, these findings have several implications. Because psychiatric outpatients were more likely than community participants to report past-year gambling, routine screening for gambling involvement and related harms in mental health services appears warranted. At the same time, the present findings suggest that psychiatric status alone should not be assumed to indicate greater gambling-related problem severity once gambling is already present.

Screening should not be limited to patients with diagnoses traditionally associated with addictions or impulse-control problems, because past-year gambling was observed across several diagnostic categories represented in the psychiatric outpatient sample. At the same time, interventions focused exclusively on reducing gambling frequency or modifying gambling-related cognitions may overlook the broader regulatory function that gambling may serve for some individuals (Buen & Flack, 2022; Raylu & Oei, 2002). Clinicians may consider assessing impulsivity in patients who report gambling; recent substance use may provide relevant clinical context. In the present data, however, only motor impulsivity was independently associated with gambling-related problem severity among past-year gamblers, whereas affective triggers were not assessed. Interventions that strengthen emotion-regulation skills and inhibitory control under emotional arousal may be relevant for individuals who gamble in response to distress (Chrétien et al., 2025; Månsson et al., 2022; Molander et al., 2026; Velotti et al., 2021), although this interpretation requires direct testing in future research. More broadly, framing gambling as a potentially affect-driven response to emotion overload, rather than only as a maladaptive behavior to be suppressed, may support a more empathic clinical stance and facilitate the development of alternative regulatory strategies (Costanzo et al., 2023; Schimmenti et al., 2025).

Limitations

First, community participants were classified solely on the basis of self-reported absence of a current psychiatric diagnosis, without a clinical diagnostic interview. This classification is vulnerable to underreporting, unrecognized conditions, and other self-report biases.

Second, emotion dysregulation and impulsivity were not assessed in community participants who reported no past-year gambling. Because these dimensions are not specific to people who gamble, their omission prevented examination of these dimensions across the entire community sample and limited interpretation of their association with gambling involvement.

Third, the cross-sectional design precludes causal inferences about the temporal relationships among psychiatric status, emotion dysregulation, impulsivity, and gambling-related problems. Longitudinal research is needed to clarify whether specific psychological profiles predict the onset and progression of gambling-related difficulties or are themselves shaped by repeated gambling and its consequences.

Fourth, psychiatric outpatients and community participants were recruited through different procedures and completed the assessment in different formats (paper-and-pencil questionnaires in psychiatric services vs. an anonymous online survey in the community group). Although meta-analytic evidence generally supports equivalence between electronic and paper-based self-report measures (Gwaltney et al., 2008; Muehlhausen et al., 2015), recommendations caution against assuming equivalence without instrument-specific evidence (Coons et al., 2009), and weaker cross-format equivalence has been reported for the BIS-11 Motor subscale in a veteran sample (Surís et al., 2007). Together with the exclusion of 178 incomplete paper-and-pencil questionnaires while online items were mandatory, these differences may have introduced mode-related and selection biases and limited the comparability of the two groups.

Fifth, although the overall sample was relatively large, it was drawn from specific psychiatric and community contexts within a single country and included relatively few community gamblers, which limits generalizability. Severity analyses were based on 235 gamblers, including 37 problem gamblers, and were therefore less precise for subgroup comparisons. Race and ethnicity were not assessed. Residual confounding may persist despite covariate adjustment, and propensity-score sensitivity analyses could not be extended to the gamblers-only severity models because common support was limited. Modest internal consistency for some DERS and BIS-11 subscales may also have attenuated associations.

Sixth, all data were based on self-report and are therefore vulnerable to recall bias, reporting bias, and social desirability effects, particularly for stigmatized behaviors such as gambling and substance use.

Seventh, gambling involvement was defined broadly as any past-year gambling, and although the CPGI provides a dimensional index of gambling-related problem severity, it is not a diagnostic assessment of GD. More fine-grained measures of gambling frequency, expenditure, modality, and game type, together with diagnostic interviews, would help differentiate subgroups with distinct clinical and risk profiles (Holtgraves, 2009; López-Torres et al., 2021). Eighth, alcohol and substance use were assessed as recent-use indicators rather than as measures of substance-related disorders. Ninth, gambling motives, trauma history, and attachment-related processes were not assessed, although they may shape the relationship between emotion dysregulation, impulsivity, and gambling-related problems (Estévez et al., 2022; Ghinassi & Casale, 2023; Neophytou et al., 2023; Santoro et al., 2025; Topino et al., 2024). Future longitudinal, multi-method studies are needed to clarify how these factors relate to gambling involvement and gambling-related problem severity.

Conclusions

The present study showed that psychiatric outpatients were more likely than community participants to report past-year gambling. However, among individuals who had gambled in the previous 12 months, psychiatric status was not independently associated with CPGI scores. In the model including all past-year gamblers, higher motor impulsivity was associated with greater gambling-related problem severity, whereas associations involving emotion dysregulation were limited and mixed. Overall, these findings are broadly consistent with process-oriented and transdiagnostic models of gambling, including the Pathways Model, which describe gambling-related problems as potentially arising through multiple pathways involving affective vulnerability and impulsivity-related externalizing features. Clinically, these results support routine screening for gambling involvement and gambling-related harms in psychiatric outpatient services, across the diagnostic categories represented in this sample, and indicate that motor impulsivity may be a useful clinical feature to consider when patients report gambling; recent substance use may provide additional clinical context, but was not independently associated with gambling involvement in the adjusted models. Longitudinal multi-method studies are needed to clarify how psychiatric status, emotion dysregulation, and impulsivity relate to gambling trajectories.

Supplementary Table S1.

Covariate balance before and after overlap weighting in the full sample

Covariate Absolute SMD before weighting Absolute SMD after weighting
Age .82 .00
Years of education .86 .00
Female gender .01 .00
Married .03 .00
Working/studying .77 .00
Not living alone .19 .00
Family history of psychiatric disorder .29 .00
Family history of problematic gambling .16 .00
Recent substance use 1.08 .00
Recent alcohol use .85 .00
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Note. SMD = standardized mean difference. Overlap weighting achieved exact balance on the covariates included in the propensity-score model.

Supplementary Table S2.

Overlap-weighted sensitivity analysis for the association between psychiatric outpatient group and past-year gambling in the full sample

Model B SE OR 95% CI p
Original adjusted logistic model 2.60 .29 13.45 [7.59, 23.84] < .01
Overlap-weighted logistic model 2.37 .44 10.69 [4.53, 25.24] < .01
Overlap-weighted adjusted logistic model 2.74 .44 15.46 [6.57, 36.38] < .01
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Note. The overlap-weighted models used propensity scores for psychiatric outpatient group estimated from age, years of education, gender, marital status, employment, living arrangement, family history of psychiatric disorder, family history of problematic gambling, recent substance use, and recent alcohol use.

Supplementary Table S3.

Nominally significant secondary exploratory associations that did not remain significant after Benjamini–Hochberg correction

Model Predictor Metric Estimate [95% CI] p qBH
Psychiatric outpatients: gambling status Age OR .98 [.96, 1.00] .04 .22
Community participants: gambling status Recent alcohol use OR 2.93 [1.15, 7.47] .02 .14
Psychiatric outpatient gamblers: CPGI total DERS impulse control difficulties B -.24 [-.44, -.04] .02 .13
Psychiatric outpatient gamblers: CPGI total BIS-11 motor impulsivity B .29 [.07, .52] .01 .09
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Note. qBH = Benjamini–Hochberg false discovery rate-adjusted p value. The Benjamini–Hochberg procedure was applied across the 44 tests from the two within-group logistic models predicting past-year gambling, the logistic regression predicting past-year gambling in psychiatric outpatients from DERS and BIS-11 subscales, and the linear regression predicting CPGI total in psychiatric outpatient gamblers. The nominal p values and 95% confidence intervals reported in the table are unadjusted; statistical significance after Benjamini–Hochberg correction was evaluated at qBH < .05.

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