The Use of Telegram in Surgical Education: Exploratory Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has disrupted medical education, shifting learning online. Social media platforms, including messaging apps, are well integrated into medical education. However, Telegram's role in medical education remains relatively unexplored. OBJECTIVE: This study aims to explore the perceptions of medical students regarding the role of messaging apps in medical education and their experience of using Telegram for surgical education. METHODS: A Telegram channel "Telegram Education for Surgery Learning and Application (TESLA)" was created to supplement medical students' learning. We invited 13 medical students who joined the TESLA channel for at least a month to participate in individual semistructured interviews. Interviews were conducted via videoconferencing using an interview guide and were then transcribed and analyzed by 2 researchers using inductive thematic content analysis. RESULTS: Two themes were identified: (1) learning as a medical student and (2) the role of mobile learning (mLearning) in medical education. Students shared that pandemic-related safety measures, such as reduced clinic allocations and the inability to cross between wards, led to a decrease in clinical exposure. Mobile apps, which included proprietary study apps and messaging apps, were increasingly used by students to aid their learning. Students favored Telegram over other messaging apps and reported the development of TESLA as beneficial, particularly for revision and increasing knowledge. CONCLUSIONS: The use of apps for medical education increased during the COVID-19 pandemic. Medical students commonly used apps to consolidate their learning and revise examination topics. They found TESLA useful, relevant, and trustworthy.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".