Critical thinking predicts reductions in Spanish physicians' stress levels and promotes fake news detection

Escolà‐Gascón, Álex; Dagnall, Neil; Gallifa, Josep

doi:10.1016/j.tsc.2021.100934

Cited by 20 publications

(19 citation statements)

References 44 publications

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“…This coincides with the evidence obtained by Bago et al (2020) , which makes our results consistent with the findings of other studies (see also Roozenbeek & van der Linden, 2019 ). Moreover, an adaptive use of critical thinking could help suggestible individuals to better regulate their emotional lability ( Escolà-Gascón et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

“…This test has previously been used in published peer reviewed research (see Escolà-Gascón, 2021 ; Escolà-Gascón, Dagnall, & Gallifa, 2021 ; Escolà-Gascón, Marín, et al, 2021 ), where it has demonstrated good validity and reliability. The mean obtained ranged from 8.13 to 10.32 (9.51 in the sample of Escolà-Gascón, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

“…The concept of “fake news” is defined as content that is disseminated or released as “real” information when it is not ( Lazer et al, 2018 ; van der Linden et al, 2020 ). In this sense, fake news is fabricated (see Escolà-Gascón, Dagnall, & Gallifa, 2021 ). This definition is similar to the delineations of pseudoscientific beliefs and conspiracy theories outlined earlier.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Who falls for fake news? Psychological and clinical profiling evidence of fake news consumers

Escolà‐Gascón

Dagnall

Denovan

et al. 2023

Personality and Individual Differences

Self Cite

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Who falls for fake news? Psychological and clinical profiling evidence of fake news consumers

Escolà‐Gascón

Dagnall

Denovan

et al. 2023

Personality and Individual Differences

Self Cite

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“…In Spain, the impact of fake news and pseudoscience was determined in 1129 doctors working in primary and emergency care, from five different Spanish communities. In Fake News detection tests, the use of critical thinking predicted a 46.9% reduction in stress levels; skeptical attitudes and critical thinking predicted 56.1% of occurrences; stress levels during the pandemic were significant; the effectiveness of detecting fake news increased by 30.7% if the individual was a doctor [21]. A study with North American and Canadian authors indicated that there is an important disconnect between what people believe and what they share on social networks, and such dissociation seems to be largely motivated by inattention rather than the intentional sharing of misinformation [22].Often, these fake news can encourage actions and behavior contrary to the guidelines of technical authorities, associated with a fragile socialization, in addition to a lack of knowledge, peer pressure and attention seeking.…”

Section: Fake News In the Covid-19 Pandemicmentioning

confidence: 99%

Fake News and the Covid-19 Pandemic: Repercussions on Nursing Assistance

Silveira¹

2022

Corpus J Clin Trials

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Objective: To carry out a theoretical reflection on how fake news related to the COVID-19 pandemic had an impact on health and nursing care. Method: theoretical-reflective study about fake news related to the COVID-19 pandemic and the impact on health and nursing care. To search for articles/texts, the descriptors Fake News, COVID-19, Coronavirus Infections, Health Repercussions extracted from Descriptors in Health Sciences (DeCS) and Medical Subject Headings (MeSH) were used in Portuguese and English. and the keyword Repercussions in Nursing. The search took place in scientific databases and databases, with a thorough reading of articles related to the theme. Results and Discussion: Three guiding points/categories were elaborated to support the theoretical-reflective text of this article: “The Fake News in the COVID-19 pandemic”; “Some interferences of Fake News in the population”, with four subdivided categories and the last category “The impact on nursing care.” Final considerations: discrediting the recommendations of health professionals, especially nurses, had an impact on their health due to the stress they experienced, the disrespect and the violence they suffered, in the face of disbelief in relation to the correct guidelines that they verbalized and followed. It is understood that educational programs to stimulate critical thinking and online behavior are important strategies for the management of Fake News, helping to further prevent the dissemination of erroneous information about relevant topics related to the pandemic.

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“…It is demonstrated by the results that Convolutional Neural Networks (CNNs) with word embedding may achieve high accuracy of up to 98.6 percent in classification tasks. In a study conducted by Escolà-Gascón et al (2021), it was discovered that critical thinking predicts reductions in stress levels among Spanish physicians and increases fake news identification. According to Lin et al (2019), a system was provided that extracts 134 features from news articles using machine learning models and deep learning models that simply rely on textual input.…”

Section: Related Workmentioning

confidence: 99%

Multi-scale Machine Learning Prediction of the Spread of Arabic Online Fake News

Aljwari

Alkaberi

Alshutayri

et al. 2022

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There are a lot of research studies that look at "fake news" from an Arabic online source, but they don't look at what makes those fake news spread. The threat grows, and at some point, it gets out of hand. That's why this paper is trying to figure out how to predict the features that make Arabic online fake news spread. It's using Naive Bayes, Logistic Regression, and Random forest of Machine Learning to do this. Online news stories that were made up were used. They are found by using Term Frequency-Inverse Document Frequency (TF-IDF). The best partition for testing and validating the prediction was chosen at random and used in the analysis. So, all three machine learning classifications for predicting fake news in Arabic online were done. The results of the experiment show that Random Forest Classifier outperformed the other two algorithms. It had the best TF-IDF with an accuracy of 86 percent. Naive Bayes had an accuracy rate of 84%, and Logistic Regression had an accuracy rate of 85%, so they all did well. As such, the model shows that the features in TF-IDF are the most essential point about the content of an online Arabic fake news.

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Critical thinking predicts reductions in Spanish physicians' stress levels and promotes fake news detection

Cited by 20 publications

References 44 publications

Who falls for fake news? Psychological and clinical profiling evidence of fake news consumers

Who falls for fake news? Psychological and clinical profiling evidence of fake news consumers

Fake News and the Covid-19 Pandemic: Repercussions on Nursing Assistance

Multi-scale Machine Learning Prediction of the Spread of Arabic Online Fake News

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