SARS-CoV-2 B.1.1.7 lineage-related perceptions, COVID-19 vaccine acceptance and travel worry among healthcare workers

Temsah, Mohamad-Hani; Barry, Mazin; Aljamaan, Fadi; An, A.; Al‐Eyadhy, Ayman; Saddik, Basema; Alsohime, Fahad; Alhaboob, Ali; Alhasan, Khalid; Alaraj, Ali; Halwani, Rabih; Jamal, Amr; Alamro, Nurah; Temsah, Reem; Alzamil, Fahad; Alsomaily, A; Ja, Al-Tawfiq

doi:10.1101/2021.01.19.21250111

Cited by 9 publications

(12 citation statements)

References 32 publications

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“…Seeking knowledge from reliable sources about the pandemic and vaccinations could significantly impact the HCWs' perceptions of vaccine acceptance. 50 Misinformation about the COVID-19 vaccine was associated with decreased vaccination acceptance among those who would otherwise definitely vaccinate. 51 It is interesting to note the differences in knowledge about vaccines by level of training.…”

Section: Discussionmentioning

confidence: 99%

Adenovirus and RNA-based COVID-19 vaccines’ perceptions and acceptance among healthcare workers in Saudi Arabia: a national survey

et al. 2021

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ObjectivesThe aim of this study was to compare the perception, confidence, hesitancy and acceptance rate of various COVID-19 vaccine types among healthcare workers (HCWs) in Saudi Arabia, a nation with Middle East respiratory syndrome coronavirus experience.DesignNational cross-sectional, pilot-validated questionnaire.SettingOnline, self-administered questionnaire among HCWs.ParticipantsA total of 2007 HCWs working in the Kingdom of Saudi Arabia participated; 1512 (75.3%) participants completed the survey and were included in the analysis.InterventionData were collected through an online survey sent to HCWs during 1–15 November 2020. The main outcome measure was HCW acceptance of COVID-19 candidate vaccines. The associated factors of vaccination acceptance were identified through a logistic regression analysis and via measurement of the level of anxiety, using the Generalised Anxiety Disorder 7 scale.ResultsAmong the 1512 HCWs who were included, 62.4% were women, 70.3% were between 21 and 40 years of age, and the majority (62.2%) were from tertiary hospitals. In addition, 59.5% reported knowing about at least one vaccine; 24.4% of the participants were sure about their willingness to receive the ChAdOx1 nCoV-19 vaccine, and 20.9% were willing to receive the RNA BNT162b2 vaccine. However, 18.3% reported that they would refuse to receive the Ad5-vectored vaccine, and 17.9% would refuse the Gam-COVID-Vac vaccine. Factors that influenced the differential readiness of HCWs included their perceptions of the vaccine’s efficiency in preventing the infection (33%), their personal preferences (29%) and the vaccine’s manufacturing country (28.6%).ConclusionsAwareness by HCWs of the several COVID-19 candidate vaccines could improve their perceptions and acceptance of vaccination. Reliable sources on vaccine efficiency could improve vaccine uptake, so healthcare authorities should use reliable information to decrease vaccine hesitancy among frontline healthcare providers.

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Section: Discussionmentioning

confidence: 99%

Adenovirus and RNA-based COVID-19 vaccines’ perceptions and acceptance among healthcare workers in Saudi Arabia: a national survey

et al. 2021

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“…The integration of artificial intelligence and large language models in systematic reviews can also affect trust in health research. We anticipate that due to limited experience, evidence users will be more cautious about the application of studies that use such tools (45,46).…”

Section: Discussionmentioning

confidence: 99%

ChatGPT for assessing risk of bias of randomized trials using the RoB 2.0 tool: A methods study

Pitre,

Jassal,

Talukdar

et al. 2023

Preprint

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BackgroundThe assessment of risk of bias is a critical component of systematic review methods. Assessing risk of bias, however, can be time- and resource-intensive. AI-based solutions may increase efficiency and reduce burden.ObjectiveTo evaluate the reliability of ChatGPT for performing risk of bias assessments of randomized trials.MethodsWe sampled recently published Cochrane systematic reviews of medical interventions (up to October 2023) that included randomized controlled trials and assessed risk of bias using the Cochrane-endorsed revised risk of bias tool for randomized trials (RoB 2.0). From each eligible review, we collected data on the risk of bias assessments for the first three reported outcomes. Using ChatGPT-4, we assessed the risk of bias for the same outcomes using three different prompts: a minimal prompt including limited instructions, a maximal prompt with extensive instructions, and an optimized prompt that was designed to yield the best risk of bias judgments. The agreement between ChatGPT’s assessments and those of the systematic reviewers was quantified using weighted kappa statistics.ResultsWe included 34 systematic reviews with 157 unique trials. We found the agreement between ChatGPT and systematic review authors for assessment of overall risk of bias to be 0.16 (95% CI: 0.01 to 0.3) for the maximal ChatGPT prompt, 0.17 (95% CI: 0.02 to 0.32) for the optimized prompt, and 0.11 (95% CI: -0.04 to 0.27) for the minimal prompt. For the optimized prompt, agreement ranged between 0.11 (95% CI: -0.11 to 0.33) to 0.29 (95% CI: 0.14 to 0.44) across risk of bias domains, with the lowest agreement for the deviations from the intended intervention domain and the highest agreement for the missing outcome data domain.ConclusionOur results suggest that ChatGPT and systematic reviewers only have “slight” to “fair” agreement in risk of bias judgments for randomized trials. ChatGPT is currently unable to reliably assess risk of bias of randomized trials. We recommend systematic reviewers avoid using ChatGPT to perform risk of bias assessments.

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“…The use of AI-supported chatbots for automated consultation could signi cantly affect the nurse-patient relationship, particularly in terms of patient trust. It is important to note that nursing is value-based, emphasizing compassion, empathy, care, respect, and dignity for the patient (24)(25)(26).…”

Section: Speci C To Health Workermentioning

confidence: 99%

Artificial Intelligence in Healthcare: A Closer Look at ChatGPT's Usages and Challenges

khonakdar,

moradimajd,

babajani

et al. 2024

Preprint

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Background Artificial intelligence, a rapidly advancing field, is characterized as a branch of computer science capable of human-like thinking and actions. Among the extensively utilized forms of AI is Natural Language Processing (NLP), enabling machines to think and communicate in a manner resembling humans. Numerous chatbots, such as Jasper chat, DialoGPT, Replika, and ChatGPT, have been introduced as part of these advancements. Methods The study employs a scoping review methodology, searching five databases with specific keywords related to ChatGPT and healthcare. Inclusion and exclusion criteria are defined, resulting in 17 relevant articles for analysis. The research process involves a thorough review, removal of duplicates, and quality assessment. Data analysis is conducted using content analysis. Results The 17 articles are categorized into three themes: Education & Evaluation, Research & Publication, and Care & Cure. Subthemes within each category detail the usages and challenges of ChatGPT in various contexts, including education, research, and patient care. Conclusion The paper evaluates the impact of ChatGPT in different domains, emphasizing its positive influence on data availability, learning experiences, and research processes. However, ethical concerns such as plagiarism, biases, and potential risks in patient care are identified. The authors advocate for the responsible use of ChatGPT, suggesting solutions like academic integrity rules and staff training.

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SARS-CoV-2 B.1.1.7 lineage-related perceptions, COVID-19 vaccine acceptance and travel worry among healthcare workers

Cited by 9 publications

References 32 publications

Adenovirus and RNA-based COVID-19 vaccines’ perceptions and acceptance among healthcare workers in Saudi Arabia: a national survey

Adenovirus and RNA-based COVID-19 vaccines’ perceptions and acceptance among healthcare workers in Saudi Arabia: a national survey

ChatGPT for assessing risk of bias of randomized trials using the RoB 2.0 tool: A methods study

Artificial Intelligence in Healthcare: A Closer Look at ChatGPT's Usages and Challenges

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