With the COVID‐19 pandemic recognized as a major threat to human health is of paramount importance to improve the vaccination uptake of the future COVID‐19 vaccine. The study extended the health belief model (HBM) using insights from trait theory and events systems theory, to examine the role of beliefs in predicting intentions to be vaccinated against COVID‐19, when a vaccine becomes available. Employees from Greece ( N = 1006) participated from October 1 to November 5, 2020, in an anonymous online factorial survey experiment. Measures of dispositional optimism, faith in intuition, risk‐taking propensity, and acquiring resources mindset were included as individual difference variables. Multilevel modeling techniques were used for data analyses. Components of HBM had significant effects on intentions to vaccinate. Two‐way interactions between severity and susceptibility beliefs and three‐way interaction among perceived severity, susceptibility, and perceived benefits were detected. In line with the events systems theory, a critical event moderated beliefs' effects on intention to vaccinate. Acquiring resources mindset emerged as important individual difference that positively related to intentions. The model explained 59 per cent of the variance in vaccination intentions. The study highlighted interaction effects among the HBM components and how critical events may moderate belief effects.

Recent empirical research has utilized the Technology Acceptance Model (TAM) to advance the understanding of doctors' and nurses' technology acceptance in the workplace. However, the majority of the reported studies are either qualitative in nature or use small convenience samples of medical staff. Additionally, in very few studies moderators are either used or assessed despite their importance in TAM based research. The present study focuses on the application of TAM in order to explain the intention to use clinical information systems, in a random sample of 604 medical staff (534 physicians) working in 14 hospitals in Greece. We introduce physicians' specialty as a moderator in TAM and test medical staff's information and communication technology (ICT) knowledge and ICT feature demands, as external variables. The results show that TAM predicts a substantial proportion of the intention to use clinical information systems. Findings make a contribution to the literature by replicating, explaining and advancing the TAM, whereas theory is benefited by the addition of external variables and medical specialty as a moderator. Recommendations for further research are discussed.

The Evidence-Based Practice Attitude Scale (EBPAS; Aarons, 2004) is a relatively new construct for the study of attitudes toward the adoption of innovation and evidence-based practices (EBPs) in mental health service settings. Despite widespread interest in measuring the attitudes of health care providers in conjunction with the adoption of EBPs, no prior research has used the EBPAS with medical doctors, a different population than that with which the scale was originally developed. In the present study, the factor structure, reliability, and validity of EBPAS scores were tested with a sample of 534 medical doctors working in 14 Greek hospitals. In addition, associations of health care provider characteristics (age, gender, medical specialty, information and communication technology use and knowledge) with EBPAS total scores are examined. Confirmatory factor analyses support the 4-factor structure of the EBPAS and provide convincing evidence for the validity of the scale. Implications and future directions are discussed.

Evidence-based practice (EBP) is an approach that influences healthcare worldwide. Systematic research in the relevant biomedical literature was conducted using the Medline-Pubmed interface until August 2012. Six studies were included in the review. All of these studies had a cross-sectional study design, and 4 of them conducted a postal survey, using different questionnaires for data collection purposes. This review supports previous literature suggesting that community nurses have a positive attitude toward EBP. However, although EBP implementation is considered to be a professional imperative, the integration of recent evidence into clinical practice seems to be a cumbersome process.

Technology readiness (TR) represents an individual's mental readiness to accept new technologies. Although the TR scale has been used in many studies, its application in the healthcare context is limited. This paper focuses on identifying the TR profiles of medical staff and to model preference TR variations with respect to computer use, computer knowledge and computer feature demands. The study reports results from a nationwide study conducted in Greece, during a three-year period, which sampled responses from 604 physicians and nurses working in 14 Greek hospitals. Exploratory Structural Equation Modelling analysis is used in order to confirm the structure of the Technology Readiness Index. The results confirm the five groups of the TR taxonomy. Statistical differences were found between classes in information and communication technology (ICT) knowledge, ICT feature demands, hours of use per week as well as ICT use performance, but not in the general use of ICT. The results facilitate comprehension of the factors, which influence the use of ICT by medical staff and, in addition, they convey important policy and managerial implications. In conclusion, medical staff should be treated according to its TR taxonomy classes in order to expedite the acceptance and use of an ICT system.