Psychometric Evaluation of GHQ-12 as a Screening Tool for Psychological Impairment of Healthcare Workers Facing COVID-19 Pandemic.

BACKGROUND: The General Health Questionnaire (GHQ) is a widely used tool, both in clinical and research settings, due to its brevity and easy administration. Researchers often adopt a dichotomous measurement method, considering a total score above or below a certain threshold. This leads to an extreme simplification of the gathered data and therefore to the loss of clinical details. In a multi-step evaluation study aimed at assessing health care workers' mental health during the Covid-19 pandemic, GHQ-12 proved to be the most effective tool to detect psychological distress compared to other scales adopted. These results led to deepen the understanding of GHQ-12 properties through a statistical study by focusing on items' properties and characteristics. METHODS: GHQ-12 responses were analyzed using Item Response Theory (IRT), a suitable method for scale assessment. Instead of considering the single overall score, in which each item accounts equally, it focuses on individual items' characteristics. Moreover, IRT models were applied combined with the latent class (LC) analysis, aiming to the determination of subgroups of individuals according to their level of psychological distress. RESULTS: GHQ-12 was administered to 990 health-care workers and responses were scored using the binary method (0-0-1-1). We applied the two-parameter logistic (2-PL) model, finding that the items showed different ways of responses and features. The latent class analysis classified subjects into three sub-groups according to their responses to GHQ-12 only: 47% of individuals with general well-being, 38% expressing signs of discomfort without severity and 15% of subjects with a high level of impairment. This result almost reproduces subjects' classification obtained after administering the six questionnaires of the study protocol. CONCLUSIONS: Accurate statistical techniques and a deep understanding of the latent factors underlying the GHQ-12 resulted in a more effective usage of such psychometric questionnaire - i.e. a more refined gathering of data and a significant time and resource efficiency. We underlined the need to maximize the extraction of data from questionnaires and the necessity of them being less lengthy and repetitive.

Robust variable selection in the framework of classification with label noise and outliers: Applications to spectroscopic data in agri-food.

Classification of high-dimensional spectroscopic data is a common task in analytical chemistry. Well-established procedures like support vector machines (SVMs) and partial least squares discriminant analysis (PLS-DA) are the most common methods for tackling this supervised learning problem. Nonetheless, interpretation of these models remains sometimes difficult, and solutions based on feature selection are often adopted as they lead to the automatic identification of the most informative wavelengths. Unfortunately, for some delicate applications like food authenticity, mislabeled and adulterated spectra occur both in the calibration and/or validation sets, with dramatic effects on the model development, its prediction accuracy and robustness. Motivated by these issues, the present paper proposes a robust model-based method that simultaneously performs variable selection, outliers and label noise detection. We demonstrate the effectiveness of our proposal in dealing with three agri-food spectroscopic studies, where several forms of perturbations are considered. Our approach succeeds in diminishing problem complexity, identifying anomalous spectra and attaining competitive predictive accuracy considering a very low number of selected wavelengths.