Sote-henkilöstön työstä palautuminen -- avovastausten analysointi luonnollisen kielen käsittelyjärjestelmällä. - NIKUNLAAKSO, RISTO; REUNA, KAISA; KORKIAKANGAS, EVELIINA; SELANDER, KIRSIKKA; LAHTI, HEIDI; LAITINEN, JAANA (Hoitotiede, 2025)
Open answers to an employee survey can be used to collect valuable information on employees' work recovery, but analysing the results is laborious. This study investigated the means of work recovery among health and social services workers and assessed how well natural language processing (NLP) can help in analysing open survey responses on recovery. The study data comprised 1559 open answers concerning work recovery of health and social services employees, collected in a job well-being survey in 2021. The open-ended answers were analysed using an NLP classification algorithm based on SBERT language model, which interprets the semantics of the texts. Additionally, ChatGPT was used to summarize the contents of the categories. To validate the results, they were compared with a content analysis conducted by two researchers. Both the classification algorithm and the researchers divided the responses into seven categories based on their content. The categories were divided into work recovery during working hours and work recovery during leisure. Algorithm-assisted analysis found similar recovery themes. However, there were large differences between the algorithm and the researchers in the classification of responses (Cohen's kappa=0.232; 95% confidence interval 0.199-0.265). The results showed that recovery requires sufficient resources, a good organisation of work and a sense of community at the workplace. The study also showed that NLP systems can help in the rapid analysis of open-ended answers, but researchers are still needed to analyse the data in more detail.