Health Care Science最新文献

{"title":"Differential effectiveness of COVID-19 health behaviors: The role of mental health conditions in mask-wearing, social distancing, and hygiene practice","authors":"Yusen Zhai,&nbsp;Xue Du","doi":"10.1002/hcs2.60","DOIUrl":"https://doi.org/10.1002/hcs2.60","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Mental health conditions are known to increase susceptibility to infectious diseases, including coronavirus disease 2019 (COVID-19). Health behaviors play a crucial role in mitigating this susceptibility. We aim to examine the differential effectiveness of COVID-19 preventive health behaviors among individuals, considering the presence or absence of specific mental health disorders.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Multivariable logistic regression with interaction terms was performed to examine whether associations between adherence to health behaviors and COVID-19 infection were conditional on depression, anxiety, or eating disorders in a national sample of adults (<i>N</i> = 61,891) from 140 US universities, 2020–2021.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Adjusting for age, race/ethnicity, and gender/sex, the effectiveness of mask-wearing was significant and comparable among individuals with and without depression, anxiety, or eating disorders. Social distancing provided significantly less protection among individuals with depression, anxiety, or eating disorders. Hygiene practice provided significantly less protection among individuals with anxiety.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Mask-wearing is robustly effective in the prevention of COVID-19 among individuals. However, social distancing and hygiene practice provide less significant protection among individuals with certain mental health conditions, suggesting the importance of prioritizing these individuals for additional preventive measures (e.g., vaccines targeting variants) and mitigation strategies (e.g., financial assistance, targeted mental health care, health education).</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 4","pages":"286-290"},"PeriodicalIF":0.0,"publicationDate":"2023-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.60","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50151333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting venous thromboembolism (VTE) risk in cancer patients using machine learning","authors":"Samir Khan Townsley,&nbsp;Debraj Basu,&nbsp;Jayneel Vora,&nbsp;Ted Wun,&nbsp;Chen-Nee Chuah,&nbsp;Prabhu R. V. Shankar","doi":"10.1002/hcs2.55","DOIUrl":"https://doi.org/10.1002/hcs2.55","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The association between cancer and venous thromboembolism (VTE) is well-established with cancer patients accounting for approximately 20% of all VTE incidents. In this paper, we have performed a comparison of machine learning (ML) methods to traditional clinical scoring models for predicting the occurrence of VTE in a cancer patient population, identified important features (clinical biomarkers) for ML model predictions, and examined how different approaches to reducing the number of features used in the model impact model performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We have developed an ML pipeline including three separate feature selection processes and applied it to routine patient care data from the electronic health records of 1910 cancer patients at the University of California Davis Medical Center.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our ML-based prediction model achieved an area under the receiver operating characteristic curve of 0.778 ± 0.006 (mean ± SD) when trained on a set of 15 features. This result is comparable with the model performance when trained on all features in our feature pool [0.779 ± 0.006 (mean ± SD) with 29 features]. Our result surpasses the most validated clinical scoring system for VTE risk assessment in cancer patients by 16.1%. We additionally found cancer stage information to be a useful predictor after all performed feature selection processes despite not being used in existing score-based approaches.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>From these findings, we observe that ML can offer new insights and a significant improvement over the most validated clinical VTE risk scoring systems in cancer patients. The results of this study also allowed us to draw insight into our feature pool and identify the features that could have the most utility in the context of developing an efficient ML classifier. While a model trained on our entire feature pool of 29 features significantly outperformed the traditionally used clinical scoring system, we were able to achieve an equivalent performance using a subset of only 15 features through strategic feature selection methods. These results are encouraging for potential applications of ML to predicting cancer-associated VTE in clinical settings such as in bedside decision support systems where feature availability may be limited.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 4","pages":"205-222"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.55","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50150385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring COVID-19 from the perspectives of healthcare personnel in Malawi","authors":"Chúk Odenigbo,&nbsp;Eric Crighton","doi":"10.1002/hcs2.58","DOIUrl":"https://doi.org/10.1002/hcs2.58","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The Coronavirus 2019 disease (COVID-19) brought many healthcare systems around the world to the point of collapse all the while putting the lives of healthcare workers at risk. This study forgoes an institutional look at healthcare to center individual healthcare personnel in Malawi to better understand (1) how the worldviews of healthcare workers impact their work in the context of COVID-19, (2) how COVID-19 impacted healthcare workers, and (3) the unique conditions faced by being a healthcare worker in a low-income nation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This research uses a hermeneutic phenomenological approach to qualitative methodology involving in-depth interviews (<i>n</i> = 15) with healthcare workers, traditional healers, and hospital leadership. The data collected were inductively coded and analyzed using the framework method, producing rich descriptions on how COVID-19 impacted the lifeworlds of healthcare workers in Malawi.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The findings reveal many of the struggles healthcare workers faced due to misaligned government policy and perceived proximity to COVID-19; outline their needs such as wanting better resources, funds, wages, and public health communication; and, exemplify the significant role that personal biases, worldviews, and sense of fear played in how healthcare workers perceived and interacted with COVID-19.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Much of what was said echoes beyond borders, reflecting common global sentiments felt by healthcare personnel, and offers directions to explore building policies, strategies, and plans in preparation for any future disease outbreaks.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 4","pages":"242-254"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.58","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50150381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Healthy lifestyles in relation to cardiometabolic diseases among schoolteachers: A cross-sectional study","authors":"Liyan Huang,&nbsp;Mengjie He,&nbsp;Jie Shen,&nbsp;Yiying Gong,&nbsp;Hui Chen,&nbsp;Xiaolin Xu,&nbsp;Geng Zong,&nbsp;Yan Zheng,&nbsp;Chao Jiang,&nbsp;Baohong Wang,&nbsp;Ronghua Zhang,&nbsp;Changzheng Yuan","doi":"10.1002/hcs2.59","DOIUrl":"https://doi.org/10.1002/hcs2.59","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>We aimed to explore the associations of adherence to an overall healthy lifestyle with cardiometabolic diseases (CMDs) among schoolteachers in China.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We conducted a cross-sectional analysis among 2983 teachers (aged 39.8 ± 9.3 years, 73.8% women) in Zhejiang Province, China. A healthy lifestyle score (0–7) was constructed based on seven low-risk factors: healthy diet, noncurrent smoking, noncurrent drinking, regular exercise, normal body mass index (BMI), adequate sleep duration, and limited sedentary behavior. CMDs included self-reported hyperlipidemia, hypertension, diabetes, coronary heart disease, and stroke. Multivariable-adjusted logistic regression models were used to evaluate the associations between healthy lifestyle and CMD.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A total of 493 (16.5%) participants had at least one CMD, with hyperlipidemia, hypertension, and diabetes being the three leading CMDs. Each point increment in a healthy lifestyle score was associated with 20% lower odds of having CMD (<i>p</i>-trend &lt; 0.001). Compared with 0–3 low-risk factors, the odds ratios (<i>OR</i>s) and 95% confidence intervals (<i>CI</i>s) were 0.66 (0.50–0.88) for 4 low-risk factors and 0.51 (0.39–0.67) for 5–7 low-risk factors. We observed independent associations for normal BMI (<i>OR</i> = 0.50, 95% <i>CI</i> = 0.40–0.63), noncurrent drinking (<i>OR</i> = 0.53, 95% <i>CI</i> = 0.36–0.77), and limited sedentary behavior (<i>OR</i> = 0.77, 95% <i>CI</i> = 0.62–0.96) in relation to CMD. Healthy diet (<i>OR</i> = 0.75, 95% <i>CI</i> = 0.55–1.01) exhibited marginally significant association with CMD.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our findings suggest that adherence to an overall healthy lifestyle is associated with lower odds of CMD among schoolteachers.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 4","pages":"223-232"},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.59","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50127343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring quality of life at work for healthcare and social services workers: A systematic review of available instruments","authors":"Liang Wang,&nbsp;Moustapha Touré,&nbsp;Thomas G. Poder","doi":"10.1002/hcs2.53","DOIUrl":"https://doi.org/10.1002/hcs2.53","url":null,"abstract":"<p>Quality of life at work is an important and widely discussed concept in the literature. Several instruments can be used to measure it, but with regard to healthcare and social services, the existing instruments are not well known. A review of available instruments intending to capture the quality of life of healthcare and social services workers (QoLHSSW) is necessary to better assess their working conditions and promote programs/guidelines to improve these conditions. The aim of this study was to identify the existing instruments used in measuring QoLHSSW and explore their characteristics. Particular attention was given to instruments adapted to the province of Quebec, Canada, which enabled the determination of which instruments are adapted for the measurement of QoLHSSW in Quebec and possibly elsewhere. A systematic review of the literature was conducted according to the JBI methodological guide. The articles' selection procedure was performed according to the PRISMA flowchart. The search was conducted up to October 28, 2021, and then updated on January 25, 2023, in four databases: PsycINFO, Medline, Embase, and CINAHL. The selection and extraction were performed independently by two researchers. The analysis of the quality of the studies was performed with the COnsensus-based Standards for the selection of health Measurement Instruments. From a total of 8178 entries, 13 articles corresponding to 13 instruments were selected. Among these instruments, the common aspects that were considered were work conditions, job satisfaction, stress at work, relationship/balance, and career development. Most instruments used a 5-point Likert scale. Various validation methods were used, including reporting Cronbach's alpha for overall scale reliability; factor analysis to test construct validity; different model fit indices to test model superiority; different language comparisons to test cross-cultural validity; and qualitative expert reviews to assess content validity.</p>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 3","pages":"173-193"},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.53","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50129741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness of online HIV treatment services in the context of the COVID-19 pandemic","authors":"Jing Han,&nbsp;Hanxi Zhang,&nbsp;Ye Su,&nbsp;Fujie Zhang","doi":"10.1002/hcs2.54","DOIUrl":"https://doi.org/10.1002/hcs2.54","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The COVID-19 pandemic has created challenges with respect to HIV care services. Remote online services might provide an effective method for health service delivery to people living with HIV (PLHIV). Few studies have focused on the efficacy of telemedical services for PLHIV and the effect of antiretroviral treatment via online services in China.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We developed a platform called the “No. 8 Health” for online antiretroviral drug collection and delivery services in Beijing from January 21 to June 30, 2022. We evaluated the online treatment service according to viral load suppression rates and compared differences in social characteristics between PLHIV who received antiretroviral drugs through online or offline treatment services.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>By June 2022, 9528 PLHIV had received outpatient treatment services, among which 44.6% (4031/9528) used the online treatment and drug delivery services for a total of 5590 person-times. The satisfaction rate was 100%. Rates of viral load suppression among PLHIV who initiated antiretroviral therapy (ART) in 2020 and 2021 were 96.4% and 93.1%, respectively. Results showed that the viral load suppression rate was 97.9%. Regarding HIV rapid self-testing, 4513 men who have sex with men used the online HIV rapid testing service. The number of users was approximately the same as in 2021, but both were slightly lower than those in 2020.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This study was the first to evaluate the effect of online drug collection and delivery services and virologic outcomes among PLHIV in China. The online service helped with maintenance of ART services, but the COVID-19 pandemic still had some impacts on viral load suppression.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 3","pages":"164-172"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.54","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50126673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolution and major changes of the diagnosis and treatment protocol for COVID-19 patients in China 2020–2023","authors":"You Wu,&nbsp;Xiaoru Feng,&nbsp;Mengchun Gong,&nbsp;Jinming Han,&nbsp;Yuanshi Jiao,&nbsp;Shenglong Li,&nbsp;Tong Li,&nbsp;Chen Shen,&nbsp;Huai-Yu Wang,&nbsp;Xinyu Yu,&nbsp;Zeyu Zhang,&nbsp;Zhengdong Zhang,&nbsp;Yuanfei Zhao,&nbsp;Peng Zhou,&nbsp;Haibo Wang,&nbsp;Zongjiu Zhang","doi":"10.1002/hcs2.45","DOIUrl":"https://doi.org/10.1002/hcs2.45","url":null,"abstract":"<p>Since the identification of the first case of pneumonia of unknown cause in 2019, the COVID-19 pandemic has spread the globe for over 3 years. As the most populous country in the world, China's disease prevention policies and response plans concern the health of the country's 1.4 billion people and beyond. During the course of the pandemic, scientific research has been accumulated and given evidence-based support to the official guidance of COVID-19 management. The National Health Commission of China have compiled, published, and updated a total of 10 versions of the “Diagnosis and Treatment Protocol for COVID-19 Patients” to better inform clinical practitioners and staff to effectively screen, diagnose, manage, treat, and care for cases of severe acute respiratory syndrome coronavirus 2 infection. This paper compares and summarizes each version of the protocol in terms of etiology and epidemiology, clinical manifestation and diagnosis, treatment and nursing, disease control and management, presenting detailed changes, additions, deletions, and refinement of the protocols.</p>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 3","pages":"135-152"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.45","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50136353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lessons learned from the hospital to home community care program in Singapore and the supporting AI multiple readmissions prediction model","authors":"John Abisheganaden,&nbsp;Kheng Hock Lee,&nbsp;Lian Leng Low,&nbsp;Eugene Shum,&nbsp;Han Leong Goh,&nbsp;Christine Gia Lee Ang,&nbsp;Andy Wee An Ta,&nbsp;Steven M. Miller","doi":"10.1002/hcs2.44","DOIUrl":"https://doi.org/10.1002/hcs2.44","url":null,"abstract":"<p>In a prior practice and policy article published in Healthcare Science, we introduced the deployed application of an artificial intelligence (AI) model to predict longer-term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home (H2H) program that has been operating since 2017. In this follow on practice and policy article, we further elaborate on Singapore's H2H program and care model, and its supporting AI model for multiple readmission prediction, in the following ways: (1) by providing updates on the AI and supporting information systems, (2) by reporting on customer engagement and related service delivery outcomes including staff-related time savings and patient benefits in terms of bed days saved, (3) by sharing lessons learned with respect to (i) analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants, (ii) balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables, and (iii) the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems, (4) by highlighting how this H2H effort supported broader Covid-19 response efforts across Singapore's public healthcare system, and finally (5) by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards. For the convenience of the reader, some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.</p>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 3","pages":"153-163"},"PeriodicalIF":0.0,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.44","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50147425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated labelling of radiology reports using natural language processing: Comparison of traditional and newer methods","authors":"Seo Yi Chng,&nbsp;Paul J. W. Tern,&nbsp;Matthew R. X. Kan,&nbsp;Lionel T. E. Cheng","doi":"10.1002/hcs2.40","DOIUrl":"https://doi.org/10.1002/hcs2.40","url":null,"abstract":"<p>Automated labelling of radiology reports using natural language processing allows for the labelling of ground truth for large datasets of radiological studies that are required for training of computer vision models. This paper explains the necessary data preprocessing steps, reviews the main methods for automated labelling and compares their performance. There are four main methods of automated labelling, namely: (1) rules-based text-matching algorithms, (2) conventional machine learning models, (3) neural network models and (4) Bidirectional Encoder Representations from Transformers (BERT) models. Rules-based labellers perform a brute force search against manually curated keywords and are able to achieve high F1 scores. However, they require proper handling of negative words. Machine learning models require preprocessing that involves tokenization and vectorization of text into numerical vectors. Multilabel classification approaches are required in labelling radiology reports and conventional models can achieve good performance if they have large enough training sets. Deep learning models make use of connected neural networks, often a long short-term memory network, and are similarly able to achieve good performance if trained on a large data set. BERT is a transformer-based model that utilizes attention. Pretrained BERT models only require fine-tuning with small data sets. In particular, domain-specific BERT models can achieve superior performance compared with the other methods for automated labelling.</p>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 2","pages":"120-128"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.40","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50153835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personality theory: New factors to incorporate in public decision-making in communities","authors":"Mengyao Yan,&nbsp;Jinzi Zhang,&nbsp;Pu Ge,&nbsp;Yibo Wu","doi":"10.1002/hcs2.43","DOIUrl":"https://doi.org/10.1002/hcs2.43","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>This study explored the effects of personality factors on public behavioral decision-making.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We examined the literature on personality theory based on triadic interaction decision theory, and summarized and compared the findings with studies of the Big Five personality characteristics. A literature review method was used to explore the implications of personality theory for public decision-making in Chinese communities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Individuals with high neuroticism can be targeted by influential communicators. Individuals with high extraversion can influence decision-making through interpersonal relationships. Individuals with high levels of openness can be influenced by the development of novel activities. Conscientious individuals respond to scientific and rational knowledge. Individuals with high agreeableness can be influenced by groups.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Personality traits can influence behavioral decisions and can have positive or negative effects on behavioral outcomes. For people with different personality traits, social actors and social activity communicators should formulate targeted measures according to the classification of personality traits. The current findings have implications for enriching research perspectives and approaches to public community decision-making.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 3","pages":"198-203"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.43","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50137565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}