Using Instagram to Enhance a Hematology and Oncology Teaching Module During the COVID-19 Pandemic: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic necessitated the rapid expansion of novel tools for digital medical education. At our university medical center, an Instagram account was developed as a tool for medical education and used for the first time as a supplement to the hematology and medical oncology teaching module of 2020/2021. OBJECTIVE: We aimed to evaluate the acceptance and role of Instagram as a novel teaching format in the education of medical students in hematology and medical oncology in the German medical curriculum. METHODS: To investigate the role of Instagram in student education of hematology and medical oncology, an Instagram account was developed as a tie-in for the teaching module of 2020/21. The account was launched at the beginning of the teaching module, and 43 posts were added over the 47 days of the teaching module (at least 1 post per day). Five categories for the post content were established: (1) engagement, (2) self-awareness, (3) everyday clinical life combined with teaching aids, (4) teaching aids, and (5) scientific resources. Student interaction with the posts was measured based on overall subscription, "likes," comments, and polls. Approval to conduct this retrospective study was obtained from the local ethics commission of the University Medical Center Goettingen. RESULTS: Of 164 medical students, 119 (72.6%) subscribed to the Instagram account, showing high acceptance and interest in the use of Instagram for medical education. The 43 posts generated 325 interactions. The highest number of interactions was observed for the category of engagement (mean 15.17 interactions, SD 5.01), followed by self-awareness (mean 14 interactions, SD 7.79). With an average of 7.3 likes per post, overall interaction was relatively low. However, although the category of scientific resources garnered the fewest likes (mean 1.86, SD 1.81), 66% (27/41) of the student participants who answered the related Instagram poll question were interested in studies and reviews, suggesting that although likes aid the estimation of a general trend of interest, there are facets to interest that cannot be represented by likes. Interaction significantly differed between posting categories (P<.001, Welch analysis of variance). Comparing the first category (engagement) with categories 3 to 5 showed a significant difference (Student t test with the Welch correction; category 1 vs 3, P=.01; category 1 vs 4, P=.01; category 1 vs 5, P=.001). CONCLUSIONS: Instagram showed high acceptance among medical students participating in the hematology and oncology teaching curriculum. Students were most interested in posts on routine clinical life, self-care topics, and memory aids. More studies need to be conducted to comprehend the use of Instagram in medical education and to define the role Instagram will play in the future. Furthermore, evaluation guidelines and tools need to be developed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".