Search

Rural outplacement programs have been offered by Australian universities to encourage dental students to consider rural careers. The dental curricula should be designed to provide graduates with a good understanding of oral health issues that rural oral health care workers face. Pre-outplacement perceptions of dental students on rural practice are considered imperative to review and update the outplacement programs; however, they have not been investigated in detail. An online, anonymous, voluntary survey was conducted among the first-year dental students. The responses were solicited on the level of awareness and understanding of rural oral health, intention to practice in rural locations and factors informing the appeal of rural dental practice. The mean of a seven-point Likert scale revealed that most students had some level of perceived knowledge and awareness of rural communities and oral health. Students with rural affiliation were more likely to have the intent to practise rurally after graduation (p = 0.001). For short-term rural practice, students overall expressed positive intention. Greater job opportunities were the main motivating factor, while the distance from family and friends was the main deterring factor for practising rurally. Rural affiliation influences the intent to practise rurally on graduation. The pre-outplacement perceptions, in this study, are comparable with post-outplacement observations reported in the literature.

Objective: The electrocardiogram (ECG) provides an effective, non-invasive approach for clinical diagnosis in patients with cardiac diseases such as atrial fibrillation (AF). AF is the most common cardiac rhythm disturbance and affects ~2% of the general population in industrialized countries. Automatic AF detection in clinics remains a challenging task due to the high inter-patient variability of ECGs, and unsatisfactory existing approaches for AF diagnosis (e.g. atrial or ventricular activity-based analyses). Approach: We have developed RhythmNet, a 21-layer 1D convolutional recurrent neural network, trained using 8528 single-lead ECG recordings from the 2017 PhysioNet/Computing in Cardiology (CinC) Challenge, to classify ECGs of different rhythms including AF automatically. Our RhythmNet architecture contained 16 convolutions to extract features directly from raw ECG waveforms, followed by three recurrent layers to process ECGs of varying lengths and to detect arrhythmia events in long recordings. Large 15  ×  1 convolutional filters were used to effectively learn the detailed variations of the signal within small time-frames such as the P-waves and QRS complexes. We employed residual connections throughout RhythmNet, along with batch-normalization and rectified linear activation units to improve convergence during training. Main results: We evaluated our algorithm on 3658 testing data and obtained an F1 accuracy of 82% for classifying sinus rhythm, AF, and other arrhythmias. RhythmNet was also ranked 5th in the 2017 CinC Challenge. Significance: Potentially, our approach could aid AF diagnosis in clinics and be used for patient self-monitoring to improve the early detection and effective treatment of AF. [ABSTRACT FROM AUTHOR]