Not Just a Medical Student: Delivering Medical Education Through a Short Video Series on Social Media
Bibliographic record
Abstract
"Not Just a Medical Student" is an innovative bite-size medical education video series founded and hosted on social media. Its primary aim is to inspire tomorrow's doctors to be creative while engaging and informing them with the latest innovations, technology, and conferences within various specialties. To our knowledge, these themes are scarcely covered in the structured medical curriculum. Created and launched in August 2017, "Not Just a Medical Student" quickly gained traction; with over 1000 followers on Facebook and a rapidly increasing number of views, it reached the medical community across the globe. The video series features a trailblazer in virtual reality surgery and its potential impact on the evolution of medical education, reviewing future medical technology apps, such as Touch Surgery, and reporting on the latest medical education and health apps. The series engaged in topical medico-politics at the British Medical Association House and reported on global health issues and innovations at the Royal Society of Medicine Conference. The video series has further received several national awards including the Association and Study of Medical Education (ASME) Educator Innovator 2017 award, runner up to the Zeshan Qureshi Outstanding Contribution to Medical Education Award, and the Alternative Docs National Social Media Influencer award. The concept has been presented at international conferences (eg, the Healthcare Leadership Academy conference) and gained international recognition upon personal invitation at the Norwegian Annual Junior Doctors Conference. With the rise of the social media generation, innovative methods to inspire, engage, and inform students contributing to the continuous evolution of medical education should be encouraged and further explored.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.013 |
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".