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In the study, predictive models for predicting therapy outcome are created using the dataset from E-COMPARED project, which belongs to the so-called type 3 models that use data from the intervention and preintervention phases to predict treatment outcomes, which can help to adapt intervention to maximize treatment. The predictive models aim to classify patients into two groups, improved and nonimproved. Since it is important to determine whether the models contribute to improvement of treatment, research questions that can contribute to the usage of type 3 models are established. The study focuses on the following three questions: (1) How accurately can the therapy outcome be predicted by various machine learning algorithms? Answering this question can let the people concerned obtain information about the reliability of contemporary predictive models. In addition, if the predictive power of the models is good, it is more likely to be used to assist therapists’ decisions. (2) Which kind of data is more important in predicting the therapy outcome? The answer to this question can show which dataset should be considered first to make better predictive models. Therefore, it can be helpful for researchers who want to make predictive models in the future and eventually help to facilitate personalized therapy. (3) What are the features with strong predictive power? The answer to this question can affect the people concerned, especially therapists. Therapists can use the most influential features revealed to adjust and improve future treatments.
The food and land use system is one of the most important global economic sectors. At the same time, today's resource-intensive agricultural practices and the profit orientation in the food value chain lead to a loss of biological diversity and ecosystem services, high emissions, and social inequality - so-called negative externalities. From a scientific perspective, there is a broad consensus on the need to transform the current food system. This paper investigates the suitability of True Cost Accounting (TCA) as an approach to inte-grating positive and negative externalities into business decisions in the food and land use system, focusing on the retail sector due to its high market power and resulting influence on externalities along the entire food value chain. For this purpose, a qualitative study was con-ducted with sustainability managers of leading European food retail companies in terms of their annual turnover, sustainable finance experts, and political actors related to environmental and social policy. A sample of N=11 participants was interviewed about the emergence and meas-urement of externalities along the food value chain, the current and future relevance of knowing about externalities for food retail companies, and the market and policy framework necessary for the application of TCA. The data collected was evaluated using the method of qualitative content analysis according to Mayring. Findings show that TCA is a suitable method for capturing positive and negative external ef-fects along the food value chain and thus also for meeting the growing social, political, and financial demands for its sustainable orientation. At the same time, there are still some chal-lenges in the application of TCA, both from a theoretical and a practical point of view. The main challenges at present are the lack of a standardised methodology, data availability, and key performance indicators. Due to the focus on prices, margins and competitors, food retail groups, in particular, emphasise the risk of revenue and profit losses as well as customer churn when applying TCA. Hence, the introduction of TCA in the food and land use system requires the development of measures that are socially acceptable, backed by legal frameworks and promote the scientific development of the methodology. This offers the opportunity to create a level playing field, apply the polluter-pays principle to the entire value chain and support science in developing appropriate indicators as well as a TCA database. Food retail companies can benefit from addressing TCA at an early stage by analysing their value chain to initiate change processes early, identify risk raw materials and products, reduce negative externalities through targeted measures, sensitise customers to the issue and thus differentiate themselves from competitors.