Table 2.
Screening tools
| Tool | Items | Purpose | Setting | Study | Outcome |
|---|---|---|---|---|---|
| 4Es | 40 items | Predict gambling risk based on factors of Escape, Esteem, Excess and Excitement; distinguish gambling risk from alcohol harms | Non-treatment seeking | Rockloff and Dyer, 2006 | In sample of 2,577 (Study 2): Escape, excitement and excess independently predict PGSI gambling problems. Escape and excess independently distinguish gambling from substance use. |
| The Alfred Screening Tool for Problem Gambling | 4 items | Screen for problem gambling (self or other) in mental health service | Mental health | de Castella, 2011 | Identified gambling harms in mental health treatment seeking population |
| ASI-G | 5 items | Assess severity of gambling problems | Gambling treatment; opioid use treatment | Petry, 2003 | In sample of 598: adequate to good Cronbach’s α (0.90); significant correlations between ASI-G scores and other indices (SOGS, DSM, TLFB) |
| BBGS | 3 items | Screen for gambling disorder (DSM-5) | Methadone maintenance treatment (MMT) outpatient clinic | Himelhoch et al., 2015 | In sample of 300: sensitivity (0.909); specificity (0.865); positive predictive value (0.821); negative predictive value (0.933) |
| Substance use treatment | Rowe et al., 2015 | Inclusion in treatment manual for ATOD workers | |||
| CHI-T | 15 items | Measure transdiagnostic compulsivity | Non-treatment seeking | Chamberlain and Grant, 2018 | The CHI-T had good convergent validity, with total scores correlating significantly with gambling disorder symptoms and with obsessive-compulsive symptoms. CHI-T total scores were also significantly elevated in participants who had a current substance use disorder versus those who did not. |
| CHAT | 9 risk factors | Develop multi-item general practice tool | Primary health | Goodyear-Smith et al., 2004 | In sample of 2,543 patients across 20 GP clinics, 2.8% self-assessed drug use harm, 3.2% gambling harm and 10.8% alcohol harm. |
| Screen for health risk factors for follow-up in primary care | Goodyear-Smith et al., 2009 | In sample of 755: alcohol: sensitivity 80%, specificity 85%; drug use: sensitivity 64%, specificity 98%; gambling: sensitivity 80%, specificity 98%. | |||
| Testing of online eCHAT tool in primary care | Goodyear-Smith et al., 2013 | In sample of 196 patients: 97% found iPad-based eCHAT easy to use; 9% had concerns about data privacy. | |||
| Testing of CHAT tool in veteran health care | Goodyear-Smith et al., 2021 |
In a sample of 34: VeCHAT tool proved acceptable to veterans and Veterans’ Affairs staff |
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| Feasibility study of the CHAT | Elley et al., 2014 | In sample of 107: CHAT found to be acceptable. 2 objections to alcohol questions; no other objections recorded. | |||
| EIGHT | 8 items | Screen for pathological gambling or problem gambling (DSM-IV) | Substance use treatment | Sullivan, 2007 | In sample of 676 in AOD setting: high correlation with SOGS (83.9%) |
| GDSQ-P | 27 items | Screen for gambling disorder (DSM-5) | Substance use treatment (residential) | Maarefvand et al., 2019 | In sample of 503: high sensitivity (0.99); specificity (0.98) and accuracy (0.98) |
| ICB | 33 items | Measure impulsive-compulsive behaviors | Non-treatment seeking | Guo et al., 2017 | In sample of 687: Impulsive-Compulsions and Compulsive-Impulsions yielded very good Cronbach’s α of 0.89 and 0.84 respectively. The ICB Checklist is best utilised by examining each item endorsed and corresponding severity rating. The total sum of each subscale may indicate whether there is a stronger inclination towards impulsive or compulsive behaviors. |
| Lie/Bet | 2 items | Screen for pathological gambling (DSM-IV) | MMT outpatient clinic | Himelhoch et al., 2015 | In sample of 300: sensitivity (0.942); specificity (0.657); positive predictive value (0.651); negative predictive value (0.944) |
| mASI | 27 items (approx. 1 h completion) | Secondary screen or assess for gambling problems and substance use problems | Substance use/ non-substance use treatment (outpatient) | Denis et al., 2016 | In sample of 833: The Cronbach’s α ranged from 0.63 to 0.87 and could be considered good for medical, alcohol, and gambling domains; acceptable for employment/ support, drug, tobacco, and psychiatric domains; and questionable for legal and family /social domains. |
| NODS | 17 items | Screen for risky gambling, harmful gambling, and pathological gambling (DSM-IV) | Substance use treatment | Wickwire et al., 2008 | In sample of 157: good Cronbach’s α (0.88); positive correlation with SOGS (r = .85, p < .001) |
| NODS-CLiP | 3 items | Screen for Loss of Control + Lying + Preoccupation items of NODS | MMT outpatient clinic | Himelhoch et al., 2015 | In sample of 300: sensitivity (1); specificity (0.539); positive predictive value (0.596); negative predictive value (1) |
| Individuals recruited through substance use settings | Volberg et al., 2011 | In sample of 375: 3-item NODS-CPR had higher diagnostic efficiency (90.1% compared to 86.4% for NODS-CLiP) | |||
| NODS-PERC | 4 items | Screen for Preoccupation + Escape + Risked Relationships + Chasing items of NODS | MMT outpatient clinic | Himelhoch et al., 2015 | In sample of 300: sensitivity (1); specificity (0.573); positive predictive value (0.614); negative predictive value (1) |
| PGSI | 9 items | Screen non-problem gambler, low-risk gambler, moderate-risk gambler, problem gambler (self-assess) | Substance use treatment | Rowe et al., 2015 | Inclusion in treatment manual for ATOD workers |
| SPQ | 160 questions (10 × 16 domains) | Assess addictive behaviors | Addiction treatment (residential) | Christo et al., 2003 | In sample of 497 (clinical)/ 508 (non-clinical): SPQ alcohol had high correlation with CAGE, SADQ, SMAST; SPQ recreation drugs scale had strong correlation with SODQ and SDS; SPQ prescription drugs had correlation with 6/8 validated scales; SPQ gambling had strong correlation with SOGS and correlation with 2 validated drug use scales. Cronbach’s α coefficient was high (between 0.82 and 0.98) across subscales. |
| University students | MacLaren and Best, 2010 | In sample of 948 university students: adequate inter-item reliability using Cronbach’s α (between 0.81 and 0.96). | |||
| SSBA |
40 questions (4 × 10 domains) |
Screen for addiction problems | Non-treatment seeking | Schluter et al., 2018 | In sample of 6,000: AUC values were moderate-to-high demonstrating overall ability of each subscale to discriminate between individuals who did/ did not self-report problematic engagement in a target behavior |
| SSOGS | 7 items | Screen for pathological gambling (DSM-III) | Substance use treatment (residential) | Nelson and Oehlert, 2008 | In sample of 316: the SOGS internal consistency (coefficient α) for all items was 0.91. The 7-item SSOGS data yielded an internal consistency of 0.79. |
| Vices’ questionnaire |
44 questions (22 × 2) |
Screen for drug and addictive behaviors (‘vices’); distinguish ‘wanting’ from ‘liking’ | Non-treatment seeking | Dale et al., 2016 | In sample of 479: the ‘liking’/’wanting’ version of the survey had higher reliability and validity than the simpler ‘desire’ assessment. |