Greek dental students' perceptions of treating older patients.

BACKGROUND AND AIM: The oral problems of the older population are largely undertreated because of various barriers, including negative perceptions and ageist stereotypes, among care providers. The aim of this study was to record the perceptions of Greek dental students of treating older patients. MATERIALS AND METHODS: An anonymous written questionnaire including two open-ended questions about the positive and negative issues when treating older patients was administered to clinical dental students. A content analysis was conducted to identify the main themes. RESULTS: A total of 135 students responded to the questionnaire (response rate 88.23%). Five key themes were identified for the facilitating factors and six for the barriers/challenging factors. The facilitating factors of treating older people were the promotion of the patients' health and quality of life, the good interaction and communication with older patients, the dentist's intrinsic reward treating older people, the positive feedback from the patients and the improvement of professional dental competences when treating complex cases. The barriers included communication and cooperation problems, treatment challenges related to disease and disability, patients' negative beliefs about oral health, barriers to accessing dental care, complex and time-consuming treatment plans, and communication and cooperation problems with the patients' carers. CONCLUSION: A range of facilitating and challenging factors influence dental students' perceptions of treating older patients. More research is necessary on the methods that will enhance their geriatric knowledge and skills, help them overcome the challenges they detected and improve their attitudes and behaviours towards treating older patients.

Translation and validation of the Greek version of an ageism scale for dental students (ASDS_Gr).

OBJECTIVES: To describe the validation of a new 27-item ageism scale for dental students in Greece. BACKGROUND: A new ageism scale for dental students has been developed by American and European Gerodontology educators and was preliminary validated in the United States. METHODS: The scale was translated into Greek and administered to 8th- and 10th-semester dental students in Athens. Principal components analysis was used to explore the internal structure of the measure; internal consistency reliability was assessed using Cronbach's α coefficient; corrected item-total correlations were calculated to decide which low contributing items should be removed from the scale; and discriminant validity was tested investigating variation in relation to demographic and educational factors. RESULTS: A total of 152 students responded to the questionnaire. The Principal component analysis offered a 15-item scale distributed into four factors that accounted for 56.4%, of the total variance, produced stronger factor loadings, a comparable amount of overall component variance and logical sets of components. The four factors produced were values/ethics about older people (four items, α = 0.71), patient compliance (four items, α = 0.72), barriers to dental care (four items, α = 0.57) and dentist-older patient interaction (three items, α = 0.64). Discriminant validity revealed statistically significant differences in factors and items related to semester of studies, gender and family's permanent residence. CONCLUSION: The preliminary validation of the Greek version of the ageing scale for dental students revealed a 15-item questionnaire that demonstrated acceptable validity and reliability and could be further tested in larger samples.

Building and evaluating resources for sentiment analysis in the Greek language.

Sentiment lexicons and word embeddings constitute well-established sources of information for sentiment analysis in online social media. Although their effectiveness has been demonstrated in state-of-the-art sentiment analysis and related tasks in the English language, such publicly available resources are much less developed and evaluated for the Greek language. In this paper, we tackle the problems arising when analyzing text in such an under-resourced language. We present and make publicly available a rich set of such resources, ranging from a manually annotated lexicon, to semi-supervised word embedding vectors and annotated datasets for different tasks. Our experiments using different algorithms and parameters on our resources show promising results over standard baselines; on average, we achieve a 24.9% relative improvement in F-score on the cross-domain sentiment analysis task when training the same algorithms with our resources, compared to training them on more traditional feature sources, such as n-grams. Importantly, while our resources were built with the primary focus on the cross-domain sentiment analysis task, they also show promising results in related tasks, such as emotion analysis and sarcasm detection.