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.

Background: Topic modeling approaches allow researchers to analyze and represent written texts. One of the commonly used approaches in psychology is latent Dirichlet allocation (LDA), which is used for rapidly synthesizing patterns of text within “big data,” but outputs can be sensitive to decisions made during the analytic pipeline and may not be suitable for certain scenarios such as short texts, and we highlight resources for alternative approaches. This review focuses on the complex analytical practices specific to LDA, which existing practical guides for training LDA models have not addressed. Objective: This scoping review used key analytical steps (data selection, data preprocessing, and data analysis) as a framework to understand the methodological approaches being used in psychology research using LDA. Methods: A total of 4 psychology and health databases were searched. Studies were included if they used LDA to analyze written words and focused on a psychological construct or issue. The data charting processes were constructed and employed based on common data selection, preprocessing, and data analysis steps. Results: A total of 68 studies were included. These studies explored a range of research areas and mostly sourced their data from social media platforms. Although some studies reported on preprocessing and data analysis steps taken, most studies did not provide sufficient detail for reproducibility. Furthermore, the debate surrounding the necessity of certain preprocessing and data analysis steps is revealed. Conclusions: Our findings highlight the growing use of LDA in psychological science. However, there is a need to improve analytical reporting standards and identify comprehensive and evidence-based best practice recommendations. To work toward this, we developed an LDA Preferred Reporting Checklist that will allow for consistent documentation of LDA analytic decisions and reproducible research outcomes.