Validity and reliability of a self-assessment scale for Dental and Oral Health student's perception of transferable skills in Australia.

BACKGROUND: The Australian Dental Council's (ADC) competency framework requires graduating dental practitioners to be competent in a number of transferable skills, which includes: being scientifically versed, technically skilled and capable of safe independent work and teamwork, whilst adhering to high ethical standards (Australian Dental Council. Professional Competencies of the Newly Qualified Dentist. Melbourne, Australia: ADC; 2016). Part of the role of dental educators is to ensure graduating students acquire requisite transferable skills, in line with regulatory requirements (Chuenjitwongsa et al. Eur J Dent Educ. 2018;22:1). In order to achieve this, it is imperative to assess students' own understanding or perception of transferable skill requirement upon graduation. The objective of this study was to develop a valid and reliable scale for this assessment. METHOD: A cohort of students drawn across three different dental programmes: undergraduate dentistry (years 1-3); post-graduate dentistry (years 4-5); and Bachelor of Dental Technology/Prosthesis, participated in this study. A self-assessment questionnaire containing relevant open- and closed-ended questions was administered. The questionnaire assessed students' perception of transferable skills for their future career and attitude towards learning and developing transferable skills. RESULT: In total, we successfully assessed 388 of the 391 students sampled (99.2% response rate), their mean age was 24.3 years (SD ± 5.7), and 53.3% were females, whilst 46.7% were males. Overall, exploratory factor analysis (EFA) extracted five factors for students' perception of current skill level, and four factors for future skill requirements. The factor structures were confirmed using confirmatory factor analysis (CFA), and the structure had a good model fit and high levels of reliability, with respect to individual dimension and content validity. CONCLUSIONS: The structure derived from the transferable skill survey administered to a cohort of dental students suggests that the transferable skill survey can be utilised as a valid and reliable screening tool to test students' perception of transferable skill requirements.

Efficacy of deep learning methods for predicting under-five mortality in 34 low-income and middle-income countries.

OBJECTIVES: To explore the efficacy of machine learning (ML) techniques in predicting under-five mortality (U5M) in low-income and middle-income countries (LMICs) and to identify significant predictors of U5M. DESIGN: This is a cross-sectional, proof-of-concept study. SETTINGS AND PARTICIPANTS: We analysed data from the Demographic and Health Survey. The data were drawn from 34 LMICs, comprising a total of n=1 520 018 children drawn from 956 995 unique households. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome measure was U5M; secondary outcome was comparing the efficacy of deep learning algorithms: deep neural network (DNN); convolution neural network (CNN); hybrid CNN-DNN with logistic regression (LR) for the prediction of child's survival. RESULTS: We found that duration of breast feeding, number of antenatal visits, household wealth index, postnatal care and the level of maternal education are some of the most important predictors of U5M. We found that deep learning techniques are superior to LR for the classification of child survival: LR sensitivity=0.47, specificity=0.53; DNN sensitivity=0.69, specificity=0.83; CNN sensitivity=0.68, specificity=0.83; CNN-DNN sensitivity=0.71, specificity=0.83. CONCLUSION: Our findings provide an understanding of determinants of U5M in LMICs. It also demonstrates that deep learning models are more efficacious than traditional analytical approach.