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Skip search results from other journals and go to results- 4 Journal of Medical Internet Research
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Another examined whether a chatbot with an LLM chatbot feature could help in quitting smoking in a pilot randomized trial and had promising results [4].
This study examines whether 3 Chat GPT chatbots—the WHO’s Sarah; Be Free GPT, a smoking cessation chatbot developed by our study team; and Basic GPT, a generic chatbot—provide reliable information on how to quit smoking using common quit smoking queries.
J Med Internet Res 2025;27:e66896
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Questioning the Yelp Effect: Mixed Methods Analysis of Web-Based Reviews of Urgent Cares
Thus, we sought to determine whether there truly is a Yelp effect by evaluating how frequently patients leave negative web-based reviews regarding a lack of antibiotic prescriptions.
To determine the prevalence of the Yelp effect, we analyzed a large sample of web-based reviews of urgent care centers in the US by calculating the proportion of negative reviews that exhibit a message regarding a lack of antibiotic prescriptions.
J Med Internet Res 2021;23(10):e29406
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The Twitter Social Mobility Index: Measuring Social Distancing Practices With Geolocated Tweets
The outbreak of SARS-Co V-2, a coronavirus that causes the disease COVID-19, has caused a pandemic on a scale unseen in a generation. Without an available vaccine to reduce transmission of the virus, public health organizations and elected officials have called on the public to practice social distancing. Social distancing is a set of practices in which individuals maintain a physical distance to reduce the number of physical contacts they encounter [1,2].
J Med Internet Res 2020;22(12):e21499
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Reddit is typically associated with a male adolescent culture as 67% of users are men and 59% of users are between the ages of 18 and 29 years [30], providing an opportunity to communicate with a group that is eligible for vaccination, at risk of HPV-associated cancers, and otherwise hard to reach. In a survey of the Reddit community [31], 83.5% of participants identified themselves as aged between 18 and 34 years. Of these, 80.4% identified themselves as male.
JMIR Public Health Surveill 2019;5(1):e12480
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We constructed a corpus of tweets relevant to HPV following the approach described in a study by Chen et al [29]. Figure 1 shows the flowchart of this data collection process. We first collected tweets that contained any of the 2 terms related to HPV (namely, “HPV” and “papillomavirus”) using the Twitter streaming API from November 11, 2014 to August 8, 2016, and further filtered out vaccine-irrelevant tweets using a statistical classifier [40].
J Med Internet Res 2018;20(9):e10244
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Message locations were identified using Carmen [20], a software package that infers tweet locations using Global Positioning System (GPS) coordinates and self-reported locations from the free text of the user biographic profiles.
JMIR Public Health Surveill 2015;1(1):e5
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