Understanding COVID-19 Impacts on the Health Workforce: AI-Assisted Open-Source Media Content Analysis.

BACKGROUND: To investigate the impacts of the COVID-19 pandemic on the health workforce, we aimed to develop a framework that synergizes natural language processing (NLP) techniques and human-generated analysis to reduce, organize, classify, and analyze a vast volume of publicly available news articles to complement scientific literature and support strategic policy dialogue, advocacy, and decision-making. OBJECTIVE: This study aimed to explore the possibility of systematically scanning intelligence from media that are usually not captured or best gathered through structured academic channels and inform on the impacts of the COVID-19 pandemic on the health workforce, contributing factors to the pervasiveness of the impacts, and policy responses, as depicted in publicly available news articles. Our focus was to investigate the impacts of the COVID-19 pandemic and, concurrently, assess the feasibility of gathering health workforce insights from open sources rapidly. METHODS: We conducted an NLP-assisted media content analysis of open-source news coverage on the COVID-19 pandemic published between January 2020 and June 2022. A data set of 3,299,158 English news articles on the COVID-19 pandemic was extracted from the World Health Organization Epidemic Intelligence through Open Sources (EIOS) system. The data preparation phase included developing rules-based classification, fine-tuning an NLP summarization model, and further data processing. Following relevancy evaluation, a deductive-inductive approach was used for the analysis of the summarizations. This included data extraction, inductive coding, and theme grouping. RESULTS: After processing and classifying the initial data set comprising 3,299,158 news articles and reports, a data set of 5131 articles with 3,007,693 words was devised. The NLP summarization model allowed for a reduction in the length of each article resulting in 496,209 words that facilitated agile analysis performed by humans. Media content analysis yielded results in 3 sections: areas of COVID-19 impacts and their pervasiveness, contributing factors to COVID-19-related impacts, and responses to the impacts. The results suggest that insufficient remuneration and compensation packages have been key disruptors for the health workforce during the COVID-19 pandemic, leading to industrial actions and mental health burdens. Shortages of personal protective equipment and occupational risks have increased infection and death risks, particularly at the pandemic's onset. Workload and staff shortages became a growing disruption as the pandemic progressed. CONCLUSIONS: This study demonstrates the capacity of artificial intelligence-assisted media content analysis applied to open-source news articles and reports concerning the health workforce. Adequate remuneration packages and personal protective equipment supplies should be prioritized as preventive measures to reduce the initial impact of future pandemics on the health workforce. Interventions aimed at lessening the emotional toll and workload need to be formulated as a part of reactive measures, enhancing the efficiency and maintainability of health delivery during a pandemic.

Cognitive Processes and Translation Quality: Evidence from Keystroke-Logging Software.

This study investigates the relationship between cognitive processes and translation quality in the context of English-Arabic translations of journalistic articles. Specifically, it explores the translation processes at the orientation, production, and revision stages and the relationship between such processing and translation quality, using keylogging software (i.e., Translog II) to record the experiment. Twenty-two translation trainees participated in the study, translating a news article from English into Arabic. Presas's (2012) rubric for assessment was used to evaluate the translation quality, and several correlation analyses were applied to the data. Findings revealed negative correlations between translation quality and online revision, translation duration, and text production. The trainees' translations demonstrated limitations in communicating the main ideas of the target text (TT) to the target language (TL). The findings also showed the trainees' focus on online revision and editing and the concentration of translation time and cognitive effort in the drafting phase. The researchers recommend including the three phases of translation (reading, drafting, and revision) into translation training courses and equipping translator trainees with the required skills for each translation stage.

A storytree-based model for inter-document causal relation extraction from news articles.

With more and more news articles appearing on the Internet, discovering causal relations between news articles is very important for people to understand the development of news. Extracting the causal relations between news articles is an inter-document relation extraction task. Existing works on relation extraction cannot solve it well because of the following two reasons: (1) most relation extraction models are intra-document models, which focus on relation extraction between entities. However, news articles are many times longer and more complex than entities, which makes the inter-document relation extraction task harder than intra-document. (2) Existing inter-document relation extraction models rely on similarity information between news articles, which could limit the performance of extraction methods. In this paper, we propose an inter-document model based on storytree information to extract causal relations between news articles. We adopt storytree information to integer linear programming (ILP) and design the storytree constraints for the ILP objective function. Experimental results show that all the constraints are effective and the proposed method outperforms widely used machine learning models and a state-of-the-art deep learning model, with F1 improved by more than 5% on three different datasets. Further analysis shows that five constraints in our model improve the results to varying degrees and the effects on the three datasets are different. The experiment about link features also suggests the positive influence of link information.

Data Exploration and Classification of News Article Reliability: Deep Learning Study.

Background: During the ongoing COVID-19 pandemic, we are being exposed to large amounts of information each day. This "infodemic" is defined by the World Health Organization as the mass spread of misleading or false information during a pandemic. This spread of misinformation during the infodemic ultimately leads to misunderstandings of public health orders or direct opposition against public policies. Although there have been efforts to combat misinformation spread, current manual fact-checking methods are insufficient to combat the infodemic. Objective: We propose the use of natural language processing (NLP) and machine learning (ML) techniques to build a model that can be used to identify unreliable news articles online. Methods: First, we preprocessed the ReCOVery data set to obtain 2029 English news articles tagged with COVID-19 keywords from January to May 2020, which are labeled as reliable or unreliable. Data exploration was conducted to determine major differences between reliable and unreliable articles. We built an ensemble deep learning model using the body text, as well as features, such as sentiment, Empath-derived lexical categories, and readability, to classify the reliability. Results: We found that reliable news articles have a higher proportion of neutral sentiment, while unreliable articles have a higher proportion of negative sentiment. Additionally, our analysis demonstrated that reliable articles are easier to read than unreliable articles, in addition to having different lexical categories and keywords. Our new model was evaluated to achieve the following performance metrics: 0.906 area under the curve (AUC), 0.835 specificity, and 0.945 sensitivity. These values are above the baseline performance of the original ReCOVery model. Conclusions: This paper identified novel differences between reliable and unreliable news articles; moreover, the model was trained using state-of-the-art deep learning techniques. We aim to be able to use our findings to help researchers and the public audience more easily identify false information and unreliable media in their everyday lives.

Evaluating keyphrase extraction algorithms for finding similar news articles using lexical similarity calculation and semantic relatedness measurement by word embedding.

A textual data processing task that involves the automatic extraction of relevant and salient keyphrases from a document that expresses all the important concepts of the document is called keyphrase extraction. Due to technological advancements, the amount of textual information on the Internet is rapidly increasing as a lot of textual information is processed online in various domains such as offices, news portals, or for research purposes. Given the exponential increase of news articles on the Internet, manually searching for similar news articles by reading the entire news content that matches the user's interests has become a time-consuming and tedious task. Therefore, automatically finding similar news articles can be a significant task in text processing. In this context, keyphrase extraction algorithms can extract information from news articles. However, selecting the most appropriate algorithm is also a problem. Therefore, this study analyzes various supervised and unsupervised keyphrase extraction algorithms, namely KEA, KP-Miner, YAKE, MultipartiteRank, TopicRank, and TeKET, which are used to extract keyphrases from news articles. The extracted keyphrases are used to compute lexical and semantic similarity to find similar news articles. The lexical similarity is calculated using the Cosine and Jaccard similarity techniques. In addition, semantic similarity is calculated using a word embedding technique called Word2Vec in combination with the Cosine similarity measure. The experimental results show that the KP-Miner keyphrase extraction algorithm, together with the Cosine similarity calculation using Word2Vec (Cosine-Word2Vec), outperforms the other combinations of keyphrase extraction algorithms and similarity calculation techniques to find similar news articles. The similar articles identified using KPMiner and the Cosine similarity measure with Word2Vec appear to be relevant to a particular news article and thus show satisfactory performance with a Normalized Discounted Cumulative Gain (NDCG) value of 0.97. This study proposes a method for finding similar news articles that can be used in conjunction with other methods already in use.

Effect of suicidal loss on bereaved individuals' suicidal ideation: Structural equation model using attitudes towards suicide scale and moderation effect of interest in news media.

INTRODUCTION: Exposure to suicidal death may cause trauma and change the bereaved family/friends' attitudes towards suicide and increase their suicide-related behavior. We aimed to examine the life-time prevalence of loss experience among the general population of South Korea, the relationship between attitudes towards suicide and suicidal intensity, and the moderation effect of interest in news media. METHODS: After analyzing 2973 structured interviews, we hypothesized structural equation model and conducted a moderation analysis. RESULTS: A total of 10.1% (n = 301) respondents had experienced the suicide of acquaintances. Acceptive attitudes such as "suicide as right" and "suicide as normal-common" were higher in the "experienced" group. All fit indices of the hypothesized model were satisfied, and experience of suicidal loss was positively associated with both acceptive attitudes and suicidal intensity. "Suicide as normal-common" positively affected suicidal intensity, but "suicide as right" was not significant. "Interest in news media" significantly moderated the relationship between loss experience and suicidal intensity. LIMITATIONS: Since our study was cross-sectional design, further longitudinal studies are needed to draw casual inferences between factors. We used the at home interview method, which might have resulted underestimated experience of suicidal loss. CONCLUSION: Our findings showed that experiencing suicide death of any acquaintances could increase individual's acceptance of suicide and also increase the risk of suicide. Frequent exposure to suicide-related news amplified their risk of suicide. To reduce the suicide risk behavior, targeted intervention with those bereaved by suicide and restriction of media reports on suicide news will be needed as prevention strategies.

Weekly ILI patient ratio change prediction using news articles with support vector machine.

BACKGROUND: Influenza continues to pose a serious threat to human health worldwide. For this reason, detecting influenza infection patterns is critical. However, as the epidemic spread of influenza occurs sporadically and rapidly, it is not easy to estimate the future variance of influenza virus infection. Furthermore, accumulating influenza related data is not easy, because the type of data that is associated with influenza is very limited. For these reasons, identifying useful data and building a prediction model with these data are necessary steps toward predicting if the number of patients will increase or decrease. On the Internet, numerous press releases are published every day that reflect currently pending issues. RESULTS: In this research, we collected Internet articles related to infectious diseases from the Centre for Health Protection (CHP), which is maintained the by Hong Kong Department of Health, to see if news text data could be used to predict the spread of influenza. In total, 7769 articles related to infectious diseases published from 2004 January to 2018 January were collected. We evaluated the predictive ability of article text data from the period of 2013-2018 for each of the weekly time horizons. The support vector machine (SVM) model was used for prediction in order to examine the use of information embedded in the web articles and detect the pattern of influenza spread variance. The prediction result using news text data with SVM exhibited a mean accuracy of 86.7 % on predicting whether weekly ILI patient ratio would increase or decrease, and a root mean square error of 0.611 on estimating the weekly ILI patient ratio. CONCLUSIONS: In order to remedy the problems of conventional data, using news articles can be a suitable choice, because they can help estimate if ILI patient ratio will increase or decrease as well as how many patients will be affected, as shown in the result of research. Thus, advancements in research on using news articles for influenza prediction should continue to be pursed, as the result showed acceptable performance as compared to existing influenza prediction researches.

A prática do ensino na Escola de Enfermagem Alfredo Pinto: um registro da mídia impressa (1946) / Pratical de la educación de la Escuela de Enfermería Alfredo Pinto: un registro en los medios imprimió(1946) / Pratical of education in the Escola de Enfermagem Alfredo Pinto: a register in the media printed (1946)

Estudo histórico social tem por objetivo analisar o conteúdo de reportagem sobre o ensino na Escola de Enfermagem Alfredo Pinto (1946). O referencial teórico tem por base a linguagem da palavra e da imagem. Metodologia - A organização dos dados da reportagem foi analisada com base em 13 subtítulos e 14 imagens fotográficas, das quais recortamos quatro com critério metodologico. Resultados: o estudo apontou para: 1) o cumprimento dos requisitos básicos, preconizados pelo Ministério da Educação e Saúde para o ensino de enfermagem e; 2) a contribuição da EEAP para o ensino da enfermagem, que além da formação profissional, remunerava seus alunos e a garantia de inclusão no mercado de trabalho.

Estudios históricos sociales tiene el objetivo analizar el contenido del reportaje de la enseñanza de la Escuela de Enfermería Alfredo Pinto (1946). El referencial teórico tiene para la base la lengua de la palabra y de la imagen. Metodología - la organización de los datos del reportaje fue analizada en base de 13 subtítulos y 14 imágenes fotográficas del sub-título y, de las cuales cortamos cuatro con el criterio de la metodología. Resultados: el estudio señaló en dirección a: 1) el cumplimiento de los requisitos básicos, preconizado por el Ministerio de la Educación y Salud para la enseñanza de la enfermería y; 2) la contribución de la EEAP para la enseñanza de la enfermería, de que más allá de la formación profesional, remuneró sus pupilas y la garantía de la inclusión en el mercado del trabajo.

Social historical study has for objective to analyze the news article content on education in the Escola de Enfermagem Alfredo Pinto (1946). The theoretical referencial has for base the language of the word and the image. Methodology - the organization of the data of the news article was analyzed on the basis of 13 subs-heading and 14 photographic images, of which we cut four with methodology criterion. Results: the study it pointed stops: 1) the fulfilment of the basic requirements, praised for the Ministry of the Education and Health for the education of nursing e; 2) the contribution of the EEAP for the education of the nursing, that beyond the professional formation, remunerated its pupils and the guarantee of inclusion in the work market.