Background: Data analysis approaches such as qualitative content analysis are notoriously time and labor intensive because of the time to detect, assess, and code a large amount of data. Tools such as ChatGPT may have tremendous potential in automating at least some of the analysis. Objective: The aim of this study was to explore the utility of ChatGPT in conducting qualitative content analysis through the analysis of forum posts from people sharing their experiences on reducing their sugar consumption. Methods: Inductive and deductive content analysis were performed on 537 forum posts to detect mechanisms of behavior change. Thorough prompt engineering provided appropriate instructions for ChatGPT to execute data analysis tasks. Data identification involved extracting change mechanisms from a subset of forum posts. The precision of the extracted data was assessed through comparison with human coding. On the basis of the identified change mechanisms, coding schemes were developed with ChatGPT using data-driven (inductive) and theory-driven (deductive) content analysis approaches. The deductive approach was informed by the Theoretical Domains Framework using both an unconstrained coding scheme and a structured coding matrix. In total, 10 coding schemes were created from a subset of data and then applied to the full data set in 10 new conversations, resulting in 100 conversations each for inductive and unconstrained deductive analysis. A total of 10 further conversations coded the full data set into the structured coding matrix. Intercoder agreement was evaluated across and within coding schemes. ChatGPT output was also evaluated by the researchers to assess whether it reflected prompt instructions. Results: The precision of detecting change mechanisms in the data subset ranged from 66% to 88%. Overall κ scores for intercoder agreement ranged from 0.72 to 0.82 across inductive coding schemes and from 0.58 to 0.73 across unconstrained coding schemes and structured coding matrix. Coding into the best-performing coding scheme resulted in category-specific κ scores ranging from 0.67 to 0.95 for the inductive approach and from 0.13 to 0.87 for the deductive approaches. ChatGPT largely followed prompt instructions in producing a description of each coding scheme, although the wording for the inductively developed coding schemes was lengthier than specified. Conclusions: ChatGPT appears fairly reliable in assisting with qualitative analysis. ChatGPT performed better in developing an inductive coding scheme that emerged from the data than adapting an existing framework into an unconstrained coding scheme or coding directly into a structured matrix. The potential for ChatGPT to act as a second coder also appears promising, with almost perfect agreement in at least 1 coding scheme. The findings suggest that ChatGPT could prove useful as a tool to assist in each phase of qualitative content analysis, but multiple iterations are required to determine the reliability of each stage of analysis.

Background and Aims: Multiple studies have examined barriers and facilitators to help‐seeking, but the prevalence of help‐seeking for problem gambling (PG) is not well established. We aimed to estimate the international prevalence of help‐seeking for PG among the general population and among subgroups of people at risk for PG (i.e. low‐risk, moderate‐risk and PG).Methods: Systematic search of grey literature (through gambling repositories, gambling research institutes and Google) and peer‐reviewed literature (through ProQuest, PsycINFO, PubMed and Scopus) for gambling prevalence studies that reported on help‐seeking for PG. This review adhered to the Preferred Reporting Items for Systematic Review and Meta‐Analyses. Studies used representative sampling methods to determine the prevalence of gambling participation and data collection 2010 onward. Twenty‐four studies met the inclusion criteria. The main outcome was population prevalence of help‐seeking for PG. Help‐seeking was defined as any intentional action to change gambling behaviours, including professional services (inclusive of in‐person or distance help), non‐professional help (e.g. from family and friends) and self‐help. Subgroup analyses were conducted to explain variability in help‐seeking prevalence estimates.Results: Measurement of help‐seeking was inconsistent across included studies and, overall, there was high risk of bias. We estimated a general population help‐seeking prevalence for PG of 0.23% (95% CI, 0.16–0.33). Prevalence estimates were significantly higher in studies assessing lifetime (0.50%; 95% CI, 0.35–0.71) compared with current help‐seeking (0.14%; 95% CI, 0.10–0.20, P < 0.001), but there was no evidence of difference in prevalence estimates by gambling participation, region, type of help‐seeking, or year of data collection. Compared with people with low‐risk gambling (0.27%; 95% CI, 0.07%–1.04%), prevalence estimates were significantly higher in those with moderate‐risk (3.73%; 95% CI, 2.07%–6.63%) and problem gambling (20.63%; 95% CI, 12.89%–31.35%, P < 0.001).Conclusions: One in 25 moderate‐risk gamblers and 1 in 5 people with problem gambling have sought help for problems related to their gambling.

Personalized Normative Feedback (PNF) may help address addictive disorders. PNF highlights discrepancies between perceived and actual peer norms, juxtaposed against self-reported behavior. PNF can be self-directed and cost-efficient. Our study estimates the efficacy of PNF alone, and in combination with other self-directed interventions, to address frequency and symptom severity of hazardous alcohol use, problem gambling, illicit drug and tobacco use. We searched electronic databases, grey literature, and reference lists of included articles, for randomized controlled trials published in English (January 2000-August 2019). We assessed study quality using the Cochrane Risk of Bias tool. Thirty-four studies met inclusion criteria (k = 28 alcohol, k = 3 gambling, k = 3 cannabis, k = 0 tobacco). Thirty studies provided suitable data for meta-analyses. PNF alone, and with additional interventions, reduced short-term alcohol frequency and symptom severity. PNF with additional interventions reduced short-term gambling symptom severity. Effect sizes were small. PNF did not alter illicit drug use. Findings highlight the efficacy of PNF to address alcohol frequency and symptom severity. The limited number of studies suggest further research is needed to ascertain the efficacy of PNF for gambling and illicit drug use. Cost-effectiveness analyses are required to determine the scale of PNF needed to justify its use in various settings.

Non-gambling specialist services, such as primary care, alcohol and other drug use, and mental health services, are well placed to enhance the identification of people with gambling problems and offer appropriate generalist first level interventions or referral. Given time and resource demands, many of these clinical services may only have the capacity to administer very short screening instruments. This systematic review was conducted to provide a resource for health service providers and researchers in identifying the most accurate brief (1-5 item) screening instruments to identify problem and at-risk gambling for their specific purposes and populations. A systematic search of peer-reviewed and grey literature from 1990 to 2019 identified 25 articles for inclusion. Meta-analysis revealed five of the 20 available instruments met criteria for satisfactory diagnostic accuracy in detecting both problem and at-risk gambling: Brief Problem Gambling Screen (BPGS-2), NODS-CLiP, Problem Gambling Severity Index-Short Form (PGSI-SF), NODS-PERC, and NODS-CLiP2. Of these, the NODS-CLiP and NODS-PERC have the largest volume of diagnostic data. The Lie/Bet Questionnaire and One-Item Screen are also promising shorter options. Because these conclusions are drawn from a relatively limited evidence base, future studies evaluating the diagnostic accuracy of existing brief instruments across settings, age groups, and timeframes are needed.

Background and aimsTo date, no systematic approach to identifying the content and characteristics of psychological interventions used to reduce gambling or problem gambling has been developed. This study aimed to develop a reliable classification system capable of identifying intervention characteristics that could, potentially, account for greater or lesser effectiveness.MethodsIntervention descriptions were content analyzed to identify common and differentiating characteristics. A coder manual was developed and applied by three independent coders to identify the presence or absence of defined characteristics in 46 psychological and self-help gambling interventions.ResultsThe final classification taxonomy, entitled Gambling Intervention System of CharacTerization (GIST), included 35 categories of intervention characteristics. These were assigned to four groups: (a) types of change techniques (18 categories; e.g., cognitive restructuring and relapse prevention), (b) participant and study characteristics (...