The effectiveness of safety observation classification: A case study

Abstract

Safety observations are an essential means of improving safety management and culture, and they play a key role in supporting the execution of occupational safety strategies. Information from observations can be obtained through, for example, case descriptions and various types of classifications. Classification enables the observations to be examined in a more structured manner. In this study, the applicability and effectiveness of the observation type classifications selected by the person who submitted the observation were analysed. The findings are based on an analysis of 1,988 safety observations made by a scaffolding service company. The observations were analysed to determine what proportion of the items classified into each category had been classified correctly, and what proportion of all observations belonging to a given category could be retrieved by examining that category. In addition, a generative artificial intelligence (GAI) was used to test how well it can categorise safety observations (n = 100). Based on the results, the individuals submitting the observations were generally able to classify them correctly when the category was easy to understand. Some categories were regarded as the most critical for the company’s operations. The safety observation classification performed by the GAI was slightly weaker compared to the original human-made classifications. The findings also indicated that, in order to make better use of the information obtained through classification, general classifications should be adapted to company- or industry-specific categories. This will ensure that the information obtained is more specific and relevant.

Keywords: Generative Artificial Intelligence, Near Miss, Occupational Health and Safety, Safety Management

DOI: 10.54941/ahfe1008200