Statistical analysis
Quantitative data was analysed using Microsoft Excel version 2205 (Microsoft, Redmond, WA, U. S. A.) and IBM SPSS Statistics version 28.0.1.1 (IBM, Armonk, NY, U. S. A). For Questionnaire part one (demographic data and participation) and part two (satisfaction), descriptive analyses were conducted to obtain means/medians, range, standard deviations and percentages. Satisfaction was asked about in an understandable way, by allowing the participants to rate and indicate the questions on satisfaction with the course and the trainer himself on a five-point scale from very satisfied to very dissatisfied.Questionnaire part three measured the learning gain of the participants in a post-then procedure. In 22 items, different aspects of the course content were given. These learning objectives were developed collaboratively within the research group, but primarily with the instructor. He could specifically state what he wants to and can achieve with the course. In addition, the literature on courses that have already been carried out and an existing mindfulness inventory were used as a guide [The post-then procedure allows the calculation of the Comparative Self-Assessment (CSA) Gain according to Raupach et al. [The CSA Gain method, used as a form of outcome measurement, identifies changes in relation to the objectives under study. Compared to other learning outcome measures that only test knowledge at a specific point in time, the calculation of Comparative Self-Assessment includes information about the participants’ initial level of performance. It therefore provides information about the growth in knowledge, skills and attitudes acquired over the course duration. Furthermore, comparative self-assessments have a high validity compared to isolated self-assessments [The formula originally published by Raupach et al. (2012) reads CSA Gain [%] = (mean pre – mean post) / (mean pre – 1) × 100. The CSA Gain gives the percentage change in the level of knowledge, skill or attitude in values ranging from -100% to + 100%. To calculate 95% confidence intervals and standard error of the CSA Gain, we calculated the individual learning gain values (ILG) first, according to Ref. 39. To this end, we used the formulas (1) ILG = 0 for pre = post, (2) ILG = (pre – post) / (pre – 1) × 100 for pre > post and (3) ILG = (pre – post) / (post – 1) × 100 for pre < post. Here, With these formulas, CSA Gain equals the mean of ILG and gives the same result as the original formula [