The dimensions of patient-centred care include not only clinical effectiveness and patient safety, but, importantly, the preferences of patients as consumers of healthcare services. A total of 249 participants were included in the study, with a balanced population proportional representation by age, gender, ethnicity and geographic region of New Zealand. An online questionnaire was used to identify participants’ decision-making process, and what factors and barriers for participants to seek dental treatment. Cross-tabulations, Spearman correlation analysis and Pearson Chi-Square analysis were used for the statistical analyses. Three most common reasons for visit were check-up (77%), clean (57%) and relief of pain 36%). A desire to treat a perceived problem was the most common encouraging factor to seek dental care. Cost was the most common barrier to seeking dental services. The majority of participants attended a private practice (84%), with convenience of location and referral from professionals the most likely to influence their choice. Participants felt the most important trait a dental practitioner could demonstrate was to discuss treatment options with them before any treatment. Dental check-up, teeth cleaning and relief of pain were the most common reasons for patients to choose dental services. Cost and ethnicity of the consumers had a significant impact on how dental services were perceived and sought. Dental practitioners may need to reorientate how they express value of oral health practice, not just in regard to communication with patients, but also with government funding agencies.

Recently, the non-intrusive speech quality assessment method has attracted a lot of attention since it does not require the original reference signals. At the same time, neural networks began to be applied to speech quality assessment and achieved good performance. To improve the performance of non-intrusive speech quality assessment, this paper proposes a neural network-based assessment method using attention pooling function. The proposed systems are based on the convolutional neural networks (CNNs), bidirectional long short-term memory (BLSTM), and CNN-LSTM structure. Comparing four types of pooling functions both theoretically and experimentally, we find the attention pooling function performs the best among the four. Experiments are conducted in a dataset containing various degraded speech signals with corresponding subjective quality scores. The results show that the proposed CNN-LSTM model using attention pooling function achieves state-of-the-art correlation coefficient (R) and root-mean-square error (RMSE) of 0.967 and 0.269, outperforming the performance of standardization ITU-T P.563 and autoencoder-support vector regression method.