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1. Factors Influencing Clinicians’ Willingness to Prescribe Pre-exposure Prophylaxis for Persons at High Risk of HIV in China: Cross-sectional Online Survey Study

Author

Cui, Sitong, Ding, Haibo, Huang, Xiaojie, Wang, Hui, Tang, Weiming, Leuba, Sequoia I, Ye, Zehao, Jiang, Yongjun, Geng, Wenqing, Xu, Junjie, and Shang, Hong

Subjects
Public aspects of medicine, RA1-1270
Abstract

BackgroundPre-exposure prophylaxis (PrEP) is an effective HIV prevention measure. Clinicians play a crucial role in PrEP implementation, and their knowledge, attitudes, and career experience may affect their willingness to prescribe PrEP. However, little is known about the attitudes and willingness of clinicians to prescribe PrEP in countries without PrEP-specific guidelines. ObjectiveWe aimed to determine the factors associated with clinicians being willing to prescribe PrEP in China. MethodsBetween May and June 2019, we conducted an online cross-sectional survey of clinicians in 31 provinces across the six administrative regions in China on the WeChat smartphone app platform. Multivariable logistic regression was used to determine factors associated with willingness to prescribe PrEP. ResultsOverall, 777 HIV clinicians completed the survey. Most of the respondents had heard of PrEP (563/777, 72.5%), 31.9% (248/777) thought that PrEP was extremely effective for reducing the risk of HIV infection, and 47.2% (367/777) thought that it was necessary to provide PrEP to high-risk groups. After adjusting for age, gender, ethnicity, and educational background of the clinicians, the following factors significantly increased the odds of the clinicians being willing to prescribe PrEP: having worked for more than 10 years, compared to 5 years or less (adjusted odds ratio [aOR] 2.82, 95% CI 1.96-4.05); having treated more than 100 patients living with HIV per month, compared to 50 patients or fewer (aOR 4.16, 95% CI 2.85-6.08); and having heard of PrEP (aOR 7.32, 95% CI 4.88-10.97). Clinicians were less likely to be willing to prescribe PrEP if they were concerned about poor adherence to PrEP (aOR 0.66, 95% CI 0.50-0.88), the lack of PrEP clinical guidelines (aOR 0.47, 95% CI 0.32-0.70), and the lack of drug indications for PrEP (aOR 0.49, 95% CI 0.32-0.76). ConclusionsAbout half of all clinicians surveyed were willing to prescribe PrEP, but most surveyed had a low understanding of PrEP. Lack of PrEP clinical guidelines, lack of drug indications, and less than 11 years of work experience were the main barriers to the surveyed clinicians’ willingness to prescribe PrEP. Development of PrEP clinical guidelines and drug indications, as well as increasing the availability of PrEP training, could help improve understanding of PrEP among clinicians and, thus, increase the number willing to prescribe PrEP.

Published
2021
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2. Context and Barriers to the Prescription of Nonoccupational Postexposure Prophylaxis Among HIV Medical Care Providers: National Internet-Based Observational Study in China

Author

Ding, Haibo, Ye, Zehao, Tang, Weiming, Huang, Xiaojie, Wang, Hui, Cui, Sitong, Jiang, Yongjun, Geng, Wenqing, Xu, Junjie, and Shang, Hong

Subjects
Public aspects of medicine, RA1-1270
Abstract

BackgroundNonoccupational postexposure prophylaxis (nPEP) is an effective HIV biomedical prevention strategy. The research and use of nPEP are mainly concentrated in the developed world, while little is known about the knowledge, attitudes, and practices of nPEP among HIV medical care providers in developing countries. ObjectiveWe aimed to assess the nPEP knowledge and prescribing practice among HIV medical care providers in mainland China. MethodsHIV medical care providers were recruited in China during May and June 2019 through an online survey regarding nPEP-related knowledge, attitudes, and clinical prescription experiences. Multivariable logistic regression was performed to identify factors associated with prescribing nPEP among HIV medical care providers. ResultsA total of 777 eligible participants participated in this study from 133 cities in 31 provinces in China. Of the participants, 60.2% (468/777) were unfamiliar with nPEP and only 53.3% (414/777) of participants ever prescribed nPEP. HIV care providers who worked in a specialized infectious disease hospital (vs general hospital, adjusted odds ratio [aOR] 2.49; 95% CI 1.85-3.37), had practiced for 6-10 years (vs 5 or fewer years, aOR 3.28; 95% CI 2.23-4.80), had practiced for 11 years or more (vs 5 or fewer years, aOR 3.75; 95% CI 2.59-5.45), and had previously prescribed occupational PEP (oPEP, aOR 4.90; 95% CI 3.29-7.29) had a significantly positive association with prescribing nPEP. However, unfamiliarity with nPEP (aOR 0.08; 95% CI 0.05-0.11), believing nPEP may promote HIV high-risk behavior (aOR 0.53; 95% CI 0.36-0.77) or result in HIV drug resistance (aOR 0.53; 95% CI 0.36-0.77) among key populations, and self-reported having no written oPEP guideline in place (aOR 0.53; 95% CI 0.35-0.79) were negatively associated with nPEP prescription behavior. ConclusionsHIV medical care providers have insufficient nPEP knowledge and an inadequate proportion of prescribing, which may impede the scale-up of nPEP services to curb HIV acquisition. The implementation of tailored nPEP training or retraining to HIV medical care providers would improve this situation.

Published
2021
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5. Correction: Risk-Taking Behaviors and Adherence to HIV Pre-Exposure Prophylaxis in Users of Geosocial Networking Apps: Real-World, Multicenter Study

Author

Wang, Hongyi, Zhang, Jing, Chu, Zhenxing, Hu, Qinghai, Dong, Willa, Huang, Xiaojie, Chen, Yaokai, Wang, Hui, He, Xiaoqing, Zhang, Lukun, Hu, Zhili, Bao, Rantong, Li, Shangcao, Li, Hang, Cui, Sitong, Jin, Xia, Ding, Haibo, Geng, Wenqing, Jiang, Yongjun, Xu, Junjie, and Shang, Hong

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Published
2020
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6. Risk-Taking Behaviors and Adherence to HIV Pre-Exposure Prophylaxis in Users of Geosocial Networking Apps: Real-World, Multicenter Study

Author

Wang, Hongyi, Zhang, Jing, Chu, Zhenxing, Hu, Qinghai, Dong, Willa, Huang, Xiaojie, Chen, Yaokai, Wang, Hui, He, Xiaoqing, Zhang, Lukun, Hu, Zhili, Bao, Rantong, Li, Shangcao, Li, Hang, Cui, Sitong, Jin, Xia, Ding, Haibo, Geng, Wenqing, Jiang, Yongjun, Xu, Junjie, and Shang, Hong

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundOver half of men who have sex with men (MSM) use geosocial networking (GSN) apps to encounter sex partners. GSN apps’ users have become a unique large subpopulation among MSM for interventions concerning HIV prevention and control. Pre-exposure prophylaxis (PrEP) is a promising measure for HIV prevention, especially for MSM, but its effectiveness largely depends on medication adherence. However, little is known about PrEP adherence among GSN apps’ users, which is critical to addressing the overall optimization of PrEP compliance outside of clinical trials in the context of large-scale implementation. ObjectiveThe objective of this study is to understand the correlation between GSN apps’ use and medication adherence among MSM receiving PrEP, with the aim to increase their awareness about PrEP use in order to increase adherence. MethodsThis study based on the China Real-world Oral intake of PrEP (CROPrEP) project, a multicenter, real-world study of Chinese MSM on daily and event-driven PrEP. Eligible participants completed a detailed computer-assisted self-interview on sociodemographic, GSN apps’ use, and sexual behavior. Then participants were followed up for 12 months and assessed for various characteristics (eg, PrEP delivery, adherence assessment, PrEP coverage of sexual activities, and regimens switch). A generalized estimation equation was used to analyze the predictors of medication adherence and regimen conversion among GSN apps’ users and nonusers. ResultsAt baseline, 756 of the 1023 eligible participants (73.90%) reported primarily using GSN apps to seek sexual partners, and GSN apps’ users are more likely to have high-risk behaviors such as multiple sex partners and condomless anal intercourse than other nonusers (all P

Published
2020
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8. Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer

Author

Jiang, Zhengbao, Gao, Luyu, Araki, Jun, Ding, Haibo, Wang, Zhiruo, Callan, Jamie, and Neubig, Graham

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Systems for knowledge-intensive tasks such as open-domain question answering (QA) usually consist of two stages: efficient retrieval of relevant documents from a large corpus and detailed reading of the selected documents to generate answers. Retrievers and readers are usually modeled separately, which necessitates a cumbersome implementation and is hard to train and adapt in an end-to-end fashion. In this paper, we revisit this design and eschew the separate architecture and training in favor of a single Transformer that performs Retrieval as Attention (ReAtt), and end-to-end training solely based on supervision from the end QA task. We demonstrate for the first time that a single model trained end-to-end can achieve both competitive retrieval and QA performance, matching or slightly outperforming state-of-the-art separately trained retrievers and readers. Moreover, end-to-end adaptation significantly boosts its performance on out-of-domain datasets in both supervised and unsupervised settings, making our model a simple and adaptable solution for knowledge-intensive tasks. Code and models are available at https://github.com/jzbjyb/ReAtt., Comment: EMNLP 2022

Published
2022

9. Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering

Author

Jiang, Zhengbao, Araki, Jun, Ding, Haibo, and Neubig, Graham

Subjects
Computer Science - Computation and Language
Abstract

Generative question answering (QA) models generate answers to questions either solely based on the parameters of the model (the closed-book setting) or additionally retrieving relevant evidence (the open-book setting). Generative QA models can answer some relatively complex questions, but the mechanism through which they do so is still poorly understood. We perform several studies aimed at better understanding the multi-hop reasoning capabilities of generative QA models. First, we decompose multi-hop questions into multiple corresponding single-hop questions, and find marked inconsistency in QA models' answers on these pairs of ostensibly identical question chains. Second, we find that models lack zero-shot multi-hop reasoning ability: when trained only on single-hop questions, models generalize poorly to multi-hop questions. Finally, we demonstrate that it is possible to improve models' zero-shot multi-hop reasoning capacity through two methods that approximate real multi-hop natural language (NL) questions by training on either concatenation of single-hop questions or logical forms (SPARQL). In sum, these results demonstrate that multi-hop reasoning does not emerge naturally in generative QA models, but can be encouraged by advances in training or modeling techniques., Comment: COLING 2022

Published
2022

18. Weakly Supervised Named Entity Tagging with Learnable Logical Rules

Author

Li, Jiacheng, Ding, Haibo, Shang, Jingbo, McAuley, Julian, and Feng, Zhe

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

We study the problem of building entity tagging systems by using a few rules as weak supervision. Previous methods mostly focus on disambiguation entity types based on contexts and expert-provided rules, while assuming entity spans are given. In this work, we propose a novel method TALLOR that bootstraps high-quality logical rules to train a neural tagger in a fully automated manner. Specifically, we introduce compound rules that are composed from simple rules to increase the precision of boundary detection and generate more diverse pseudo labels. We further design a dynamic label selection strategy to ensure pseudo label quality and therefore avoid overfitting the neural tagger. Experiments on three datasets demonstrate that our method outperforms other weakly supervised methods and even rivals a state-of-the-art distantly supervised tagger with a lexicon of over 2,000 terms when starting from only 20 simple rules. Our method can serve as a tool for rapidly building taggers in emerging domains and tasks. Case studies show that learned rules can potentially explain the predicted entities.

Published
2021

22. GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition

Author

Zhao, Xinyan, Ding, Haibo, and Feng, Zhe

Subjects
Computer Science - Computation and Language
Abstract

Instead of using expensive manual annotations, researchers have proposed to train named entity recognition (NER) systems using heuristic labeling rules. However, devising labeling rules is challenging because it often requires a considerable amount of manual effort and domain expertise. To alleviate this problem, we propose \textsc{GLaRA}, a graph-based labeling rule augmentation framework, to learn new labeling rules from unlabeled data. We first create a graph with nodes representing candidate rules extracted from unlabeled data. Then, we design a new graph neural network to augment labeling rules by exploring the semantic relations between rules. We finally apply the augmented rules on unlabeled data to generate weak labels and train a NER model using the weakly labeled data. We evaluate our method on three NER datasets and find that we can achieve an average improvement of +20\% F1 score over the best baseline when given a small set of seed rules., Comment: Accepted at EACL 2021

Published
2021

23. How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering

Author

Jiang, Zhengbao, Araki, Jun, Ding, Haibo, and Neubig, Graham

Subjects
Computer Science - Computation and Language
Abstract

Recent works have shown that language models (LM) capture different types of knowledge regarding facts or common sense. However, because no model is perfect, they still fail to provide appropriate answers in many cases. In this paper, we ask the question "how can we know when language models know, with confidence, the answer to a particular query?" We examine this question from the point of view of calibration, the property of a probabilistic model's predicted probabilities actually being well correlated with the probabilities of correctness. We examine three strong generative models -- T5, BART, and GPT-2 -- and study whether their probabilities on QA tasks are well calibrated, finding the answer is a relatively emphatic no. We then examine methods to calibrate such models to make their confidence scores correlate better with the likelihood of correctness through fine-tuning, post-hoc probability modification, or adjustment of the predicted outputs or inputs. Experiments on a diverse range of datasets demonstrate the effectiveness of our methods. We also perform analysis to study the strengths and limitations of these methods, shedding light on further improvements that may be made in methods for calibrating LMs. We have released the code at https://github.com/jzbjyb/lm-calibration., Comment: TACL 2021

Published
2020

27. X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models

Author

Jiang, Zhengbao, Anastasopoulos, Antonios, Araki, Jun, Ding, Haibo, and Neubig, Graham

Subjects
Computer Science - Computation and Language
Abstract

Language models (LMs) have proven surprisingly successful at capturing factual knowledge by completing cloze-style fill-in-the-blank questions such as "Punta Cana is located in _." However, while knowledge is both written and queried in many languages, studies on LMs' factual representation ability have almost invariably been performed on English. To assess factual knowledge retrieval in LMs in different languages, we create a multilingual benchmark of cloze-style probes for 23 typologically diverse languages. To properly handle language variations, we expand probing methods from single- to multi-word entities, and develop several decoding algorithms to generate multi-token predictions. Extensive experimental results provide insights about how well (or poorly) current state-of-the-art LMs perform at this task in languages with more or fewer available resources. We further propose a code-switching-based method to improve the ability of multilingual LMs to access knowledge, and verify its effectiveness on several benchmark languages. Benchmark data and code have been released at https://x-factr.github.io., Comment: EMNLP 2020

Published
2020

42. Thermoreversible aging in asphalt binders.

Author

Liu, Meifang, Nili, Azuo, Ding, Haibo, Feng, Xiongkai, and Qiu, Yanjun

Subjects
CRACKING of asphalt pavements, BINDING agents, DETERIORATION of materials, MECHANICAL behavior of materials, ASPHALT pavements
Abstract

It is an undisputed fact that aging increases the risk of low-temperature cracking in asphalt pavement. Previous studies on aging have focused on oxidative aging, but few studies on thermoreversible aging have been reported. The existing literature has shown that thermoreversible aging has the same effect as oxidative aging on cracking of asphalt pavement in cold region, so it is obvious that the study of thermoreversible aging is of great practical significance to improve cracking of asphalt pavement. This paper introduces the current status of thermoreversible aging research from the concept, mechanisms, characterisation methods and effects, presents the problems of existing research and indicates the direction of further research, which is important for accurately characterising the mechanical properties of asphalt binders, controlling the waste addition ratio and improving the durability of asphalt pavement. [ABSTRACT FROM AUTHOR]

Published
2024
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