Exploring the Impact of LearnENT’s Social Media Team as a Powerful Tool in Otolaryngology Medical Education
Bibliographic record
Abstract
ImportanceAdvancements in medical education have led to the adoption of virtual learning. Certain medical fields, including Otolaryngology-Head and Neck Surgery (OHNS), are underrepresented in undergraduate medical education curricula. LearnENT, an OHNS educational app, addresses this gap and has gained a global user base through its active social media presence.ObjectivesTo investigate the efficacy of the LearnENT social media team in disseminating OHNS educational resources and engaging users.DesignA longitudinal observational study with data collection conducted over an 8 month period surrounding the implementation of the social media team.SettingThe study utilized 2 platforms: Instagram Business tools and the LearnENT application dashboard.ParticipantsInstagram followers and LearnENT app users globally, with data segmented by demographics and professional backgrounds.Exposures or InterventionAnalysis of Instagram Business data, including trends in followers, accounts reached, accounts engaged, impressions, and activity on the LearnENT application dashboard.Main Outcome MeasuresFollower demographics, audience activity patterns, account interactions, and app usage trends.ResultsThe Instagram account achieved a 49.7% increase in followers (900 total) over 8 months. Engagement metrics showed an 87% rise in accounts reached and a 70% increase in impressions. App usage increased by 12%, reaching a total of 8257 users across 36 countries. Key content types, such as "Question of the Week," received the highest engagement rates.ConclusionThe LearnENT social media team has effectively disseminated OHNS educational resources and engaged learners, as evidenced by a 49.7% increase in followers and 12% increase app users.RelevanceThe global reach and diversity of the LearnENT Instagram community highlight its potential to connect individuals worldwide. Strategies to further enhance engagement include creating visually-appealing graphics, addressing audience preferences, and collaborating with other OHNS-related accounts.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".