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Influence of socio-demographic characteristics on the evaluation of effectiveness of medical simulation

Ann Agric Environ Med. 2025 Sep 18;32(3):377-382. doi: 10.26444/aaem/207636. Epub 2025 Jul 8.

ABSTRACT

INTRODUCTION AND OBJECTIVE: Learning effectiveness is a key element in the educational process that determines how effectively students can assimilate, store, and apply the knowledge acquired. There are many approaches and theories in the research literature exploring the different aspects of this process. Factors influencing learning effectiveness include learning style, motivation, learning techniques, and learning environment. Learning effectiveness also depends on individual student characteristics, including socio-demographic characteristics. The aim of the study is to verify the influence of socio-demographic characteristics on the assessment of the effectiveness of medical simulation as a learning method, using the standardised EPQ tool.

MATERIAL AND METHODS: The study was conducted between 2023-2024 among 306 nursing students by means of a diagnostic survey, using the survey instrument EPQ.

RESULTS: The surveyed students rated the educational techniques best in terms of collaboration. Statistical analysis showed a weak negative correlation between age and the evaluation of active learning, expectations, and the overall evaluation of educational techniques. Statistically significant results were obtained in the correlation of place of residence with the evaluation of educational practices.

CONCLUSIONS: The study showed a general relationship of the influence of selected socio-demographic characteristics on the evaluation of educational practices in medical simulation. Despite the occurrence of a relationship, the age of the subjects did not determine the outcome of the simulation effectiveness evaluation. There is a lack of detailed research in the available literature on the influence of socio-demographic variables on the evaluation of medical simulation educational practices, which allowed the identification of a research gap (white gap).

PMID:41025183 | DOI:10.26444/aaem/207636

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