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Reliability, Validity, and Measurement Invariance of the General Anxiety Disorder Scale Among Chinese Medical University Students.

ABSTRACT: Background: Medical students are affected by high levels of general anxiety disorder. However, few studies have specifically focused on the applicability of universal anxiety screening tools in this sample. This study was aimed to evaluate the psychometric property of the 7-item Generalized Anxiety Disorder Scale (GAD-7) among Chinese medical university students. Methods: A questionnaire survey was conducted among 1,021 medical postgraduates from six polyclinic hospitals. Internal consistency and convergent validity of the GAD-7 were evaluated. Factor analyses were used to test the construct validity of the scale. An item response theory (IRT) framework was used to estimate the parameters of each item. Multi-group confirmatory analyses and differential item function analyses were used to evaluate the measurement equivalence of the GAD-7 across age, gender, educational status, and residence. Results: Cronbach's α coefficient was 0.93 and the intraclass correlation coefficients ranged from 0.71 to 0.87. The GAD-7 summed score was significantly correlated with measures of depression symptoms, perceived stress, sleep disorders, and life satisfaction. Parallel analysis and confirmatory factor analysis supported the one-factor structure of the GAD-7. Seven items showed appropriate discrimination and difficulty parameters. The GAD-7 showed good measurement equivalence across demographic characteristics. The total test information of the scale was 22.85, but the test information within the range of mild symptoms was relatively low. Conclusions: The GAD-7 has good reliability, validity, and measurement invariance among Chinese medical postgraduate students, but its measurement precision for mild anxiety symptoms is insufficient.

SUBMITTER: Zhang C

PROVIDER: S-EPMC8170102 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIt is unlikely that applications of artificial intelligence (AI) will completely replace physicians. However, it is very likely that AI applications will acquire many of their roles and generate new tasks in medical care. To be ready for new roles and tasks, medical students and physicians will need to understand the fundamentals of AI and data science, mathematical concepts, and related ethical and medico-legal issues in addition with the standard medical principles. Nevertheless, there is no valid and reliable instrument available in the literature to measure medical AI readiness. In this study, we have described the development of a valid and reliable psychometric measurement tool for the assessment of the perceived readiness of medical students on AI technologies and its applications in medicine.MethodsTo define medical students' required competencies on AI, a diverse set of experts' opinions were obtained by a qualitative method and were used as a theoretical framework, while creating the item pool of the scale. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were applied.ResultsA total of 568 medical students during the EFA phase and 329 medical students during the CFA phase, enrolled in two different public universities in Turkey participated in this study. The initial 27-items finalized with a 22-items scale in a four-factor structure (cognition, ability, vision, and ethics), which explains 50.9% cumulative variance that resulted from the EFA. Cronbach's alpha reliability coefficient was 0.87. CFA indicated appropriate fit of the four-factor model (?2/df?=?3.81, RMSEA?=?0.094, SRMR?=?0.057, CFI?=?0.938, and NNFI (TLI)?=?0.928). These values showed that the four-factor model has construct validity.ConclusionsThe newly developed Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was found to be valid and reliable tool for evaluation and monitoring of perceived readiness levels of medical students on AI technologies and applications. Medical schools may follow 'a physician training perspective that is compatible with AI in medicine' to their curricula by using MAIRS-MS. This scale could be benefitted by medical and health science education institutions as a valuable curriculum development tool with its learner needs assessment and participants' end-course perceived readiness opportunities.

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Reliability, Validity, and Measurement Invariance of the General Anxiety Disorder Scale Among Chinese Medical University Students.

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