Dataset Information

To tweet or not to tweet, that is the question: A randomized trial of Twitter effects in medical education.

ABSTRACT: INTRODUCTION:Many medical education journals use Twitter to garner attention for their articles. The purpose of this study was to test the effects of tweeting on article page views and downloads. METHODS:The authors conducted a randomized trial using Academic Medicine articles published in 2015. Beginning in February through May 2018, one article per day was randomly assigned to a Twitter (case) or control group. Daily, an individual tweet was generated for each article in the Twitter group that included the title, #MedEd, and a link to the article. The link delivered users to the article's landing page, which included immediate access to the HTML full text and a PDF link. The authors extracted HTML page views and PDF downloads from the publisher. To assess differences in page views and downloads between cases and controls, a time-centered approach was used, with outcomes measured at 1, 7, and 30 days. RESULTS:In total, 189 articles (94 cases, 95 controls) were analyzed. After days 1 and 7, there were no statistically significant differences between cases and controls on any metric. On day 30, HTML page views exhibited a 63% increase for cases (M = 14.72, SD = 63.68) when compared to controls (M = 9.01, SD = 14.34; incident rate ratio = 1.63, p = 0.01). There were no differences between cases and controls for PDF downloads on day 30. DISCUSSION:Contrary to the authors' hypothesis, only one statistically significant difference in page views between the Twitter and control groups was found. These findings provide preliminary evidence that after 30 days a tweet can have a small positive effect on article page views.

SUBMITTER: Maggio LA

PROVIDER: S-EPMC6795488 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

To tweet or not to tweet, that is the question: A randomized trial of Twitter effects in medical education.

Maggio Lauren A LA Leroux Todd C TC Artino Anthony R AR

PloS one 20191016 10

<h4>Introduction</h4>Many medical education journals use Twitter to garner attention for their articles. The purpose of this study was to test the effects of tweeting on article page views and downloads.<h4>Methods</h4>The authors conducted a randomized trial using Academic Medicine articles published in 2015. Beginning in February through May 2018, one article per day was randomly assigned to a Twitter (case) or control group. Daily, an individual tweet was generated for each article in the Twi ...[more]

PMID: 31618267

Similar Datasets

Project description:Despite concerns about their health risks, e‑cigarettes have gained popularity in recent years. Concurrent with the recent increase in e‑cigarette use, social media sites such as Twitter have become a common platform for sharing information about e-cigarettes and to promote marketing of e‑cigarettes. Monitoring the trends in e‑cigarette-related social media activity requires timely assessment of the content of posts and the types of users generating the content. However, little is known about the diversity of the types of users responsible for generating e‑cigarette-related content on Twitter.The aim of this study was to demonstrate a novel methodology for automatically classifying Twitter users who tweet about e‑cigarette-related topics into distinct categories.We collected approximately 11.5 million e‑cigarette-related tweets posted between November 2014 and October 2016 and obtained a random sample of Twitter users who tweeted about e‑cigarettes. Trained human coders examined the handles' profiles and manually categorized each as one of the following user types: individual (n=2168), vaper enthusiast (n=334), informed agency (n=622), marketer (n=752), and spammer (n=1021). Next, the Twitter metadata as well as a sample of tweets for each labeled user were gathered, and features that reflect users' metadata and tweeting behavior were analyzed. Finally, multiple machine learning algorithms were tested to identify a model with the best performance in classifying user types.Using a classification model that included metadata and features associated with tweeting behavior, we were able to predict with relatively high accuracy five different types of Twitter users that tweet about e‑cigarettes (average F1 score=83.3%). Accuracy varied by user type, with F1 scores of individuals, informed agencies, marketers, spammers, and vaper enthusiasts being 91.1%, 84.4%, 81.2%, 79.5%, and 47.1%, respectively. Vaper enthusiasts were the most challenging user type to predict accurately and were commonly misclassified as marketers. The inclusion of additional tweet-derived features that capture tweeting behavior was found to significantly improve the model performance-an overall F1 score gain of 10.6%-beyond metadata features alone.This study provides a method for classifying five different types of users who tweet about e‑cigarettes. Our model achieved high levels of classification performance for most groups, and examining the tweeting behavior was critical in improving the model performance. Results can help identify groups engaged in conversations about e‑cigarettes online to help inform public health surveillance, education, and regulatory efforts.

Project description:BackgroundThe amount of medical and clinical-related information on the Web is increasing. Among the different types of information available, social media-based data obtained directly from people are particularly valuable and are attracting significant attention. To encourage medical natural language processing (NLP) research exploiting social media data, the 13th NII Testbeds and Community for Information access Research (NTCIR-13) Medical natural language processing for Web document (MedWeb) provides pseudo-Twitter messages in a cross-language and multi-label corpus, covering 3 languages (Japanese, English, and Chinese) and annotated with 8 symptom labels (such as cold, fever, and flu). Then, participants classify each tweet into 1 of the 2 categories: those containing a patient's symptom and those that do not.ObjectiveThis study aimed to present the results of groups participating in a Japanese subtask, English subtask, and Chinese subtask along with discussions, to clarify the issues that need to be resolved in the field of medical NLP.MethodsIn summary, 8 groups (19 systems) participated in the Japanese subtask, 4 groups (12 systems) participated in the English subtask, and 2 groups (6 systems) participated in the Chinese subtask. In total, 2 baseline systems were constructed for each subtask. The performance of the participant and baseline systems was assessed using the exact match accuracy, F-measure based on precision and recall, and Hamming loss.ResultsThe best system achieved exactly 0.880 match accuracy, 0.920 F-measure, and 0.019 Hamming loss. The averages of match accuracy, F-measure, and Hamming loss for the Japanese subtask were 0.720, 0.820, and 0.051; those for the English subtask were 0.770, 0.850, and 0.037; and those for the Chinese subtask were 0.810, 0.880, and 0.032, respectively.ConclusionsThis paper presented and discussed the performance of systems participating in the NTCIR-13 MedWeb task. As the MedWeb task settings can be formalized as the factualization of text, the achievement of this task could be directly applied to practical clinical applications.

Project description:Body weight (BW) reduction through energy restriction is ineffective at impacting the obesity epidemic. Shifting from an obesity treatment to weight gain prevention focus may be more effective in decreasing the burden of adult obesity.This was a 1-year randomized controlled trial of weight gain prevention in healthy premenopausal women, aged 18-45 y, with a body mass index (BMI) of >18.5 kg/m(2). Eighty-seven women were randomized to a weight gain prevention intervention delivered by a registered dietitian (RDG) or counselor (CSG), or to a control (CON) group. Eighty-one women (mean ± SD, age: 31.4 ± 8.1 y; BW: 76.1 ± 19.0 kg; BMI: 27.9 ± 6.8 kg/m(2)) completed baseline testing and were included in intention-to-treat analyses; anthropometric, blood pressure, dietary intake and physical activity measurements and biochemical markers of health were measured every three months. Data were analyzed using repeated measures ANCOVA, with significance at P < 0.01.Sixty-two percent of women met the weight gain prevention criteria (BW change within ±3 %) after one year; this did not differ by group assignment. Body fat % was lower in the RDG versus CSG and CON groups at all intervals (P < 0.001). Systolic blood pressure increased from month 6 to 9 and decreased from month 6 to 12 in the CON group (P < 0.001), with a significant group x time interaction (P < 0.01). Estimated carbohydrate intake (%) was higher in the RDG vs. CON group at month 9 (P < 0.01); fat intake (%) was lower in the RDG vs. CON group and CSG vs. CON group at months 3 and 9, respectively (P < 0.01). Estimated fruit intake (svgs/d) was higher in the RDG vs. CON group at months 3, 6, 9 and 12 (P < 0.01), and non-meat protein sources (svgs/d) was higher in the RDG vs. CSG and CON groups at month 3 (P < 0.001). Estimated energy, macronutrient and food group intakes did not change over time.A majority of all participants maintained BW over one year and were able to do so regardless of whether they received nutrition education. Additional studies that include a variety of clinical outcomes are needed to evaluate further aspects of nutrition education on weight gain prevention and health status over the long term.

Project description:BackgroundThe stigma of mental illness among medical students is a prevalent concern that has far reaching negative consequences. Attempts to combat this stigma through educational initiatives have had mixed results. This study examined the impact of a one-time contact-based educational intervention on the stigma of mental illness among medical students and compared this with a multimodal undergraduate psychiatry course at the University of Calgary, Canada that integrates contact-based educational strategies. Attitudes towards mental illness were compared with those towards type 2 diabetes mellitus (T2DM).MethodA cluster-randomized trial design was used to evaluate the impact of contact-based educational interventions delivered at two points in time. The impact was assessed by collecting data at 4 time points using the Opening Minds Scale for Health Care Providers (OMS-HC) to assess changes in stigma.ResultsBaseline surveys were completed by 62% (n=111) of students before the start of the course and post-intervention ratings were available from 90 of these. Stigma scores for both groups were significantly reduced upon course completion (p < 0.0001), but were not significantly changed following the one-time contact based educational intervention in the primary analysis. Student confidence in working with people with a mental illness and interest in a psychiatric career was increased at the end of the course. Stigma towards mental illness remained greater than for T2DM at all time points.ConclusionsPsychiatric education can decrease the stigma of mental illness and increase student confidence. However, one-time, contact-based educational interventions require further evaluation in this context. The key components are postulated to be contact, knowledge and attention to process, where attending to the student's internal experience of working with people with mental illness is an integral factor in modulating perceptions of mental illness and a psychiatric career.

Project description:Background:We hypothesized that medical students trained in suturing using high-fidelity models (cadaveric tissue) would demonstrate greater proficiency when compared with those trained using low-fidelity models (synthetic tissue). Methods:Forty-three medical students were randomized into 2 groups. Group 1 consisted of students taught to perform simple interrupted sutures using synthetic tissue, and group 2 consisted of those taught using human cadaveric tissue. Suturing proficiency was measured pre- and postinstruction using the Global Rating Scale and by measuring suture accuracy. Perceived confidence in suturing was measured on a scale of 0-100. Results:Perceived confidence was measured as an average of 8.26 out of 100 pretraining and significantly improved after training (56.91 out of 100); however, there was no significant difference when comparing confidence between groups posttraining (57.65 cadaveric versus 56.05 synthetic; P = 0.78), nor in the measured confidence change pre- and posttraining (P = 0.53). Posttraining, participants displayed a significant improvement in the number of adequately placed sutures; however, there was no significant difference posttraining when comparing groups (2.43 cadaveric versus 2.75 synthetic; P = 0.48). The change in adequate suture placement pre- and posttraining did not reach statistical significance between groups (P = 0.27). After instruction, participants demonstrated a significant improvement in total suture performance scores; however, there was no significant difference when comparing groups (30.04 cadaveric versus 29.80 synthetic; P = 0.90), nor in the total change pre- and posttraining (P = 0.74). Conclusions:Training medium fidelity (tissue versus synthetic) does not significantly influence a student's overall suturing performance. However, formal instruction significantly improves objective competence and perceived confidence. Regardless of the model, surgical departments should emphasize medical student exposure to basic surgical skills education.

Dataset Information

To tweet or not to tweet, that is the question: A randomized trial of Twitter effects in medical education.

Publications

To tweet or not to tweet, that is the question: A randomized trial of Twitter effects in medical education.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets