Urology Reach on Social Media: Appealing to Future Potential Applicants
Bibliographic record
Abstract
ObjectiveOn average, internet users aged 16 to 29 years spend 3 hours per day on social media platforms. Previous research has identified social media as an important tool for prospective applicants in the age of virtual residency interviews, but no study to date has included TikTok as a social media platform of interest. TikTok is the fastest-growing social network in the United States, and there were predictions it would reach 1.8 billion users by the end of 2022. This study seeks to understand the difference in reach of Facebook, Instagram, Twitter, and TikTok to inform medical student engagement efforts.MethodsA binary (Yes/No) poll was posted on MCG Urology accounts on Facebook, Twitter, Instagram, and TikTok. The poll asked the question “Are you a medical student?” and was open for viewing and/or response on each platform for 24 hours. The number of total views and the number and percentage of respondents were recorded for each application. Engagement was determined by the percentage of viewers who responded to the poll.ResultsA total of 3038 views and 839 responses were collected from all social media platforms. TikTok had the highest number of views (1838) and responses (617) but low engagement (33.56%). The highest percentage of “Yes” responses was on Twitter (61%); however, Twitter had the lowest engagement of 7.2%. Results of a chi-square test showed that while the total raw number of medical students reached was highest on TikTok, of all those who engaged with the poll, there were statistically significantly more medical students on Twitter (P < 0.0001).ConclusionsMedical student outreach can be successfully conducted through social media. Twitter allows for engagement with a statistically significantly larger proportion of medical students, and TikTok allows access to a grossly larger audience of medical students. Urology residency programs should consider the utility of both Twitter and TikTok for student outreach.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".