Many fields—including psychology, sociology, communications, political science, and computer science—use computational methods to analyze text data. However, existing text analysis methods have a number of shortcomings. Dictionary methods, while easy to use, are often not very accurate when compared to recent methods. Machine learning models, while more accurate, can be difficult to train and use. This research demonstrates that the large-language model GPT is capable of accurately detecting various psychological constructs (as judged by manual annotators) in text across 12 languages, using simple prompts and no additional training data. GPT thus overcomes the limitations present in existing methods. GPT is also effective in several lesser-spoken languages, which could facilitate text analysis research from understudied contexts.
Sub-Saharan Africa bears the greatest burden of HIV, with comorbid mental conditions highly prevalent in people living with HIV. It is important to evaluate the mental health of adolescents and young adults living with HIV (AYALHIV) comprehensively by measuring both negative and positive psychological constructs. There has been a proliferation of interest in positive psychological outcome measures, but the evidence of their psychometric robustness is fragmented. This review sought to: 1) Identify positive psychological outcomes and corresponding outcome measures used in AYALHIV in sub-Saharan Africa. 2) Critically appraise the psychometrics of the identified outcome measures.
Two early career scholars affiliated with the ISSBD will be awarded grants to pursue research exploring the science of caring and character strengths related to caring across the world.