Visual Note Taking for Medical Students in the Age of Instagram
Bibliographic record
Abstract
Visual note taking is a strategy used by medical students in the process of learning medicine. Some medical students show a preference for learning by drawing and some also promote learning in others by sharing their drawings globally on social media platforms. Instagram is a popular platform for promoting shared visual learning by medical students but little is known about the impact for medical student learners of these shared Instagram drawings. In this descriptive exploratory study medical students who post on Instagram were interviewed, and their followers surveyed, to determine the efficacy of both their self and shared learning. The interview transcripts and survey responses were analyzed inductively, generating themes about the role of visual learning in medicine and the larger value in building community. Both medical students who post on Instagram and their followers self-identified as having a preference for visual learning; they associated learning and retention with both the act of making and looking at, drawings. They also identified a role for mnemonics and humour in their education. Importantly, they and their followers, reported that the emergence of an online community of practice played a vital role in helping them cope with the stresses of medical training. Creating visual notes/cartoons was an integral part of the learning strategy for medical students, as was looking at these images for the followers. Through the sharing of their images, medical students acted as role models for their medical student followers, illustrating that visual note taking can be a successful learning strategy in the study of medicine. Connecting online through sharing and commenting on Instagram drawings provided a virtual space for recognition and processing of the struggles inherent in medical training.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".