Characterizing barriers to CPR training attainment using Twitter

McGovern, Shaun K; Blewer, Audrey L; Murray, Andrew; Leary, Marion; Abella, Benjamin S.; Merchant, Raina M.

doi:10.1016/j.resuscitation.2018.03.010

Cited by 11 publications

(11 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…79 Twitter was reported to promote learning during courses and was able to track CPR training barriers. 80 YouTube contains many videos about CPR education, but these videos provide insufficient information about BLS and advanced life support in adults and children according to current CPR guidelines. 81,82 In a systematic review of freely available mobile phone applications (Apps) giving instructions to perform adult CPR, only 15% adhered to current AHA or ERC guidelines.…”

Section: Technology-enhanced Education To Teach Resuscitationmentioning

confidence: 99%

European Resuscitation Council Guidelines 2021: Education for resuscitation

et al. 2021

View full text Add to dashboard Cite

These European Resuscitation Council education guidelines, are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. This section provides guidance to citizens and healthcare professionals with regard to teaching and learning the knowledge, skills and attitudes of resuscitation with the ultimate aim of improving patient survival after cardiac arrest.

show abstract

Section: Technology-enhanced Education To Teach Resuscitationmentioning

confidence: 99%

European Resuscitation Council Guidelines 2021: Education for resuscitation

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In recent years, researchers have started to use social media to study public awareness about certain illnesses, such as lung cancer [ 21 ] and cardiovascular disease [ 22 ]. It has also been used to explore public perceptions about health care (eg, cardiopulmonary resuscitation [ 23 ], vaccines [ 24 ]), and the application of new technologies in disease treatment (eg, virtual reality [ 25 ]).…”

Section: Introductionmentioning

confidence: 99%

Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media

Gao¹,

He²,

Chen³

et al. 2020

J Med Internet Res

121

View full text Add to dashboard Cite

Background High-quality medical resources are in high demand worldwide, and the application of artificial intelligence (AI) in medical care may help alleviate the crisis related to this shortage. The development of the medical AI industry depends to a certain extent on whether industry experts have a comprehensive understanding of the public’s views on medical AI. Currently, the opinions of the general public on this matter remain unclear. Objective The purpose of this study is to explore the public perception of AI in medical care through a content analysis of social media data, including specific topics that the public is concerned about; public attitudes toward AI in medical care and the reasons for them; and public opinion on whether AI can replace human doctors. Methods Through an application programming interface, we collected a data set from the Sina Weibo platform comprising more than 16 million users throughout China by crawling all public posts from January to December 2017. Based on this data set, we identified 2315 posts related to AI in medical care and classified them through content analysis. Results Among the 2315 identified posts, we found three types of AI topics discussed on the platform: (1) technology and application (n=987, 42.63%), (2) industry development (n=706, 30.50%), and (3) impact on society (n=622, 26.87%). Out of 956 posts where public attitudes were expressed, 59.4% (n=568), 34.4% (n=329), and 6.2% (n=59) of the posts expressed positive, neutral, and negative attitudes, respectively. The immaturity of AI technology (27/59, 46%) and a distrust of related companies (n=15, 25%) were the two main reasons for the negative attitudes. Across 200 posts that mentioned public attitudes toward replacing human doctors with AI, 47.5% (n=95) and 32.5% (n=65) of the posts expressed that AI would completely or partially replace human doctors, respectively. In comparison, 20.0% (n=40) of the posts expressed that AI would not replace human doctors. Conclusions Our findings indicate that people are most concerned about AI technology and applications. Generally, the majority of people held positive attitudes and believed that AI doctors would completely or partially replace human ones. Compared with previous studies on medical doctors, the general public has a more positive attitude toward medical AI. Lack of trust in AI and the absence of the humanistic care factor are essential reasons why some people still have a negative attitude toward medical AI. We suggest that practitioners may need to pay more attention to promoting the credibility of technology companies and meeting patients’ emotional needs instead of focusing merely on technical issues.

show abstract

“…2. The biggest obstacle in CPR training is that people do not accurately understand aspects such as the duration of chest compressions and the press position [6]. When using the Rescue Anne mannequin the depth of the actual press cannot be visualized.…”

Section: Introductionmentioning

confidence: 99%

Immersive Virtual Reality-Based Cardiopulmonary Resuscitation Interactive Learning Support System

et al. 2020

View full text Add to dashboard Cite

Traditional cardiopulmonary resuscitation training does not allow learners to accurately grasp power, depth and frequency. Therefore, this study aims to improve traditional training and reduce training volume, weight and price by using VR-CPRs learning support system. This study also provide immersive learning at a price lower than that currently prevalent in the market to impart the same level of learning and make CPR training more common. Our system makes it easier for individuals to learn cardiopulmonary resuscitation, making cardiopulmonary resuscitation training more common. Our system includes video health education, assisted real-time interactive operation tools, learning tests and other self-learning functions. Learners can practice on their own after watching the training film. The system reverberates the learner's power and frequency through a force-sensitive model to ensure that they are properly trained. Encourage users to learn CPR skills through immersive virtual reality settings that guide somatosensory interactions and support game-based learning. The study collected 100 college students who had never received CPR training. Before performing cardiopulmonary resuscitation on the force-sensitive model, participants were asked to complete a pre-test questionnaire to obtain pre-intervention learning outcomes. We introduced VR-CPR into CPR learning (intervention), assessed the outcome of the intervention, and asked participants to complete a post-test questionnaire. The study used VR-CPR to intensively study 85% of participants, indicating that the system is effective in promoting CPR learning, cultivating CPR learning interest, and improving public CPR ability.

show abstract

Characterizing barriers to CPR training attainment using Twitter

Cited by 11 publications

References 18 publications

European Resuscitation Council Guidelines 2021: Education for resuscitation

European Resuscitation Council Guidelines 2021: Education for resuscitation

Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media

Immersive Virtual Reality-Based Cardiopulmonary Resuscitation Interactive Learning Support System

Contact Info

Product

Resources

About