BackgroundLow survey response rates in general practice are common and lead to loss of power, selection bias, unexpected budgetary constraints and time delays in research projects.MethodsObjective: To assess the effectiveness of recruitment strategies aimed at increasing survey response rates among GPs.Design: Systematic review.Search methods: MEDLINE (OVIDSP, 1948-2012), EMBASE (OVIDSP, 1980-2012), Evidence Based Medicine Reviews (OVIDSP, 2012) and references of included papers were searched. Major search terms included GPs, recruitment strategies, response rates, and randomised controlled trials (RCT).Selection criteria: Cluster RCTs, RCTs and factorial trial designs that evaluate recruitment strategies aimed at increasing GP survey response rates.Data collection and analysis: Abstracts identified by the search strategy were reviewed and relevant articles were retrieved. Each full-text publication was examined to determine whether it met the predetermined inclusion criteria. Data extraction and study quality was assessed by using predetermined checklists.ResultsMonetary and nonmonetary incentives were more effective than no incentive with monetary incentives having a slightly bigger effect than nonmonetary incentives. Large incentives were more effective than small incentives, as were upfront monetary incentives compared to promised monetary incentives. Postal surveys were more effective than telephone or email surveys. One study demonstrated that sequentially mixed mode (online survey followed by a paper survey with a reminder) was more effective than an online survey or the combination of an online and paper survey sent similtaneously in the first mail out. Pre-contact with a phonecall from a peer, personalised packages, sending mail on Friday, and using registered mail also increased response rates in single studies. Pre-contact by letter or postcard almost reached statistical signficance.ConclusionsGP survey response rates may improve by using the following strategies: monetary and nonmonetary incentives, larger incentives, upfront monetary incentives, postal surveys, pre-contact with a phonecall from a peer, personalised packages, sending mail on Friday, and using registered mail. Mail pre-contact may also improve response rates and have low costs. Improved reporting and further trials, including sequential mixed mode trials and social media, are required to determine the effectiveness of recruitment strategies on GPs' response rates to surveys.

In recent years, along with the dramatic developments of deep learning in the natural language processing (NLP) domain, there are notable multilingual pre-trained language techniques have been proposed. These recent multilingual text analysis and mining models have demonstrated state-of-the-art performances in several primitive NLP's tasks including cross-lingual text classification (CLC). However, these recent multilingual pre-trained language models still suffered limitations which are involved in the simplicity to be adapted for specific task-driven fine-tuning in the context of low-resource languages. Moreover, they also encountered problems related to the capability of preserving the global semantic (e.g., topic, etc.) and long-range relationships between words to better fine-tune for effectively handling cross-lingual text classification task. To meet these challenges, in this paper, we proposed a novel topic-driven multi-typed text graph attention-based representation learning method for dealing with cross-lingual text classification problem, called as: TG-CTC. In our proposed TG-CTC model, we utilize a novel fused topic-driven multi-typed text graph representation to jointly learn the rich-schematic structural and global semantic information of texts to effectively fulfill the CLC task. In more specifics, we integrate the heterogeneous text graph attention network (GAT) with the neural topic modelling approach to enrich the semantic information of learnt textual representations in context of multi-language. Extensive experiments in benchmark multilingual datasets showed the effectiveness of our proposed TG-CTC model in comparing with the contemporary state-of-the-art baselines.

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Recent advanced deep learning architectures, such as neural seq2seq, transformer, etc. have demonstrated remarkable improvements in multi-typed sentiment classification tasks. Even though recent transformerbased and seq2seq-based models have successfully enabled to capture rich-contextual information of texts, they are still lacking of attention on incorporating the global semantic information, such as topic, in order to sufficiently leverage the performance of downstream SA task. Moreover, emotional expressions of users are normally in forms of natural human-written textual data which might consist a lot of noise and ambiguity which impose great challenges on the processes of textual representation learning as well as sentiment polarity prediction. To meet these challenges, we propose a novel integrated fuzzy-neural architecture with a topic-driven textual representation learning approach for handling SA task, called as: TopFuzz4SA. Specifically, in the proposed TopFuzz4SA model, we first apply a topic-driven neural encoder-decoder architecture with the incorporation of latent topic embedding and attention mechanism to sufficiently learn both rich contextual and global semantic information of the given textual data. Then, the achieved rich semantic representations of texts are fed into a fused deep fuzzy neural network to effectively reduce the feature ambiguity and noise, forming the final textual representations for sentiment classification task. Extensive experiments in benchmark datasets demonstrate the effectiveness of our proposed TopFuzz4SA model in comparing with contemporary state-of-the-art baselines.