Tweeting during conferences: educational or just another distraction?
Bibliographic record
Abstract
Social media tools are increasingly common in medical education and provide a variety of new facilities. One interesting use might be for the dissemination of information from medical education conferences. Therefore, learning how these tools, specifically Twitter, are being used to disseminate information would be informative to conference planners. The purpose of this study was to analyse the information disseminated using Twitter during the 2013 Canadian Conference on Medical Education (CCME), which is the largest meeting of this type in Canada. Are tweeters chatting about medical education topics related to the conference or just creating a background of white noise? To measure the impact of Twitter, we used TweetReach.com and analysed the CCME 2013 official conference hashtags (#): #CCME13. The # symbol, called a hashtag, is used to mark keywords or topics in a tweet. We analysed the #ccme13 hashtag 24 hours before, during and 24 hours after the conference. This analysis generated 3090 tweets from 288 different tweeters. These tweets included 1569 regular tweets, 1160 retweets, and 361 replies to another tweet. Then we analysed the frequencies of other hashtags associated with #ccme13 in order of appearance. The most common hashtag was #MedEd (= medical education), which was found in 451 tweets (14.6% of total tweets), followed by #PaperTiger (= hashtags used during one of the conference symposiums), found in 231 tweets (7.5%), #MedEdPatientsafety (94 tweets, 3.0%), and #WelcomePlenary (93 tweets, 3.0%). The most common non-education hashtag was #top200thingsILoveaboutQuebec, which ranked at 24 in our list of most used hashtags (12 tweets, 0.4%). Next we studied the hashtags used by the largest numbers of the 288 contributors (Tweeters). #MedEd was used by 119 (41.3%) contributors, followed by #PaperTiger (used by 33 contributors [11.5%]) and #sgm2013 (= Spring General Meeting of the Canadian Federation of Medical Students) (used by 30 contributors [10.4%]). The non-education #top200thingsILoveaboutQuebec was tweeted by only 3 contributors (1.0%). The analysis of tweets at CCME 2013 showed that Twitter was used to discuss the medical education themes related to the conference more often than for other purposes. This supports the idea that Twitter may be a useful tool for facilitating discussions related to conference topics. Furthermore, evidence of this use of Twitter supports the suggestion that conference organisers should implement new innovations that would facilitate the use of social networking tools in the dissemination of relevant and useful information to a potentially wider audience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.044 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".