Communicating Exercise Oncology Research in the Digital Age: Presenting the Exercise Oncology Twitter Conference
Bibliographic record
Abstract
Scientists and healthcare professionals are utilizing social media to amplify their scientific impact, acquire and share information, and communicate research to a broader audience. As such, researchers are looking for ways to engage this medium to promote scientific findings while also providing networking opportunities, particularly when costs associated with conference travel are high. PURPOSE: To examine the use of a Twitter Conference as a means to effectively communicate advances in the field of exercise oncology. METHODS: The Exercise Oncology Twitter Conference (ExOncTC) occurred in October, 2018. Each presentation consisted of six tweets over 15 minutes, each using the official conference hashtag (#ExOncTC). Attendees were able to interact during a presentation via the conference hashtag. Website registration data was used to descriptively characterize presenters and registered participants while Twitter Analytics (twitter.com) and Union Metrics (unionmetrics.com) were used to aggregate data to determine engagement and reach. RESULTS: The ExOncTC featured 68 presenters from 13 countries and 48 unique institutions. Presenters varied in academic background, ranging from undergraduate students (1.5%) to terminal degree holders (46%), and profession (inc. professors/researchers (42.5%) and M.D.s (6%)). Participants, including researchers, physicians, students, patients, and cancer organizations, could officially register via the website (n=231), follow the @ExOncTC Twitter handle (n=805), or search the conference hashtag (#ExOncTC). During the conference, #ExOncTC was tweeted 1,501 times by 483 unique users for 4,943 total engagements (number of times a user interacts with a tweet). Collectively, these tweets reached 453,900 unique users and 145,000 impressions (number of times users saw a tweet) with potential impressions equaling 1.8 million (total number of views possible). CONCLUSIONS: Total reach of the ExOncTC demonstrates the potential effectiveness of utilizing a Twitter conference as a platform to communicate the field of exercise oncology. When considering the low financial and environmental costs, as well as the opportunity to increase scientific communication across populations, Twitter conferencing should be explored as a tool for scientific dissemination.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".