Content Validation of the Self-Medication Scale and Trust in Online Resources: Deepening Digital Access to Health

Abstract

(1) Background: The rise of online health resources and AI has reshaped the diagnosis and treatment of disease, altering the relationship between patients and healthcare professionals and encouraging self-medication. However, questionnaires validated in the literature on self-medication seem to lack questions on the possible causes that induce such behaviors, including items that explore trust toward websites and artificial intelligence. The aim of this study is to design and validate the content of a questionnaire designed to assess self-medication in health professionals, delving into the underlying etiologies, the pharmacological categories involved and the degree of confidence placed in clinical assessments derived from AI tools. (2) Methods: Validation study of the content of an instrument consisting of two phases: 1. The content validation phase involved evaluation by a selected group of health experts, who, using a Likert-type scale, analyzed the clarity, coherence and relevance of the items. 2. Pilot study of health professionals who have indicated the practice of self-administration of medications. (3) Results: In the first phase of the study, the experts considered most of the questionnaire items to be clear, representative and consistent with the construct to be measured. In its second phase, the preliminary results of our pilot study suggest a significant trend toward self-medication among healthcare workers, along with a strong inclination to use online resources to search for health-related information. (4) Conclusions: The development of a validated instrument to measure the influence of the different causes that lead healthcare personnel to practice self-medication, guaranteeing validity and efficacy, allows us to establish strategies to reduce this growing problem.

Keywords

• health professionals
• self-medication
• artificial intelligence
• validation content