RESUMO
BACKGROUND: This multilevel meta-analytic review is designed to analyze and quantify the effect size of the association between technostress and work-life fit among employees. METHOD: By adhering to the PRISMA 2020 Statement, seven databases (i.e., PubMed, PsycARTICLES, PsycINFO, the Psychology and Behavioral Sciences Collection, MEDLINE, Wiley Online Library, and Web of Science) were searched for studies reporting the association between technostress and work-life fit. RESULTS: Out of the 476 articles that were screened, 7 met the established criteria and were subsequently included in this multilevel meta-analytic review. A majority of these studies highlighted the existence and intensity of the association between technostress and work-life fit, as evidenced by multiple Pearson's r. Our findings supported a medium association (Cohen's d = -0.41). Noteworthy differences were found when considering the interdependence of effect sizes within and between studies. CONCLUSION: The results of this multilevel meta-analytic review underscore the significance of understanding this association to inform optimal choices in terms of welfare policies and organizational decisions that promote employee well-being. This knowledge may serve as a scientific foundation for viewing new technologies not merely as hurdles but also as potential resources.
RESUMO
BACKGROUND: As acceptance of artificial intelligence [AI] platforms increases, more patients will consider these tools as sources of information. The ChatGPT architecture utilizes a neural network to process natural language, thus generating responses based on the context of input text. The accuracy and completeness of ChatGPT3.5 in the context of inflammatory bowel disease [IBD] remains unclear. METHODS: In this prospective study, 38 questions worded by IBD patients were inputted into ChatGPT3.5. The following topics were covered: [1] Crohn's disease [CD], ulcerative colitis [UC], and malignancy; [2] maternal medicine; [3] infection and vaccination; and [4] complementary medicine. Responses given by ChatGPT were assessed for accuracy [1-completely incorrect to 5-completely correct] and completeness [3-point Likert scale; range 1-incomplete to 3-complete] by 14 expert gastroenterologists, in comparison with relevant ECCO guidelines. RESULTS: In terms of accuracy, most replies [84.2%] had a median score of ≥4 (interquartile range [IQR]: 2) and a mean score of 3.87 [SD: ±0.6]. For completeness, 34.2% of the replies had a median score of 3 and 55.3% had a median score of between 2 and <3. Overall, the mean rating was 2.24 [SD: ±0.4, median: 2, IQR: 1]. Though groups 3 and 4 had a higher mean for both accuracy and completeness, there was no significant scoring variation between the four question groups [Kruskal-Wallis test p > 0.05]. However, statistical analysis for the different individual questions revealed a significant difference for both accuracy [pâ <â 0.001] and completeness [pâ <â 0.001]. The questions which rated the highest for both accuracy and completeness were related to smoking, while the lowest rating was related to screening for malignancy and vaccinations especially in the context of immunosuppression and family planning. CONCLUSION: This is the first study to demonstrate the capability of an AI-based system to provide accurate and comprehensive answers to real-world patient queries in IBD. AI systems may serve as a useful adjunct for patients, in addition to standard of care in clinics and validated patient information resources. However, responses in specialist areas may deviate from evidence-based guidance and the replies need to give more firm advice.