A probabilistic reliable linguistic PROBID method for selecting electronic mental health platforms considering users' bounded rationality

Reports of deteriorating mental health among citizens constitute an urgent global health alert. Electronic mental health (EMH) platforms are a great resource for mitigating mental health disorders. Extant literature on EMH has determined the attributes and indicators that reflect the feasibility, acceptability and safety of EMH platforms. However, these evaluation indicators are complex and usually conflict each other in the decision -making process. Moreover, the evaluation indicators are qualitative in nature and may be characterized by fuzziness and imprecision. Therefore, selecting an EMH platform becomes a complex cognitive process. Given the ubiquity of EMH platforms and the absence of a comprehensive decision-making model, we present a novel probabilistic reliable linguistic multi-attribute decision making (PRLMADM) model. With this model, we employ the probabilistic reliable linguistic term sets (PRLTSs) to solve decision makers' (DMs) uncertainty and reliability of evaluation information. We construct a novel Pearson correlation measure for PRLTSs and extend it to design a weighting method known as probabilistic reliable linguistic CRiteria Importance Through Inter-criteria Correlation (PRL-CRITIC). Employing the TODIM (an acronym in Portuguese for Interactive Multicriteria Decision Making) and the PROBID (Preference Ranking on the Basis of Ideal-average Distance) techniques, we propose the probabilistic reliable linguistic (PRL) TODIM-based PROBID ranking approach, which considers the DMs' bounded rationality. Finally, we present an illustrative example of EMH platform selection to prove the applicability of the proposed model. We demonstrate the benefits of our probabilistic reliable linguistic TODIM-based PROBID model through a series of comparisons and sensitivity analyses.