Maintaining a Twitter Feed to Advance an Internal Medicine Residency Program’s Educational Mission
Bibliographic record
Abstract
BACKGROUND: Residency programs face many challenges in educating learners. The millennial generation's learning preferences also force us to reconsider how to reach physicians in training. Social media is emerging as a viable tool for advancing curricula in graduate medical education. OBJECTIVE: The authors sought to understand how social media enhances a residency program's educational mission. METHODS: While chief residents in the 2013-2014 academic year, two of the authors (PB, AN) maintained a Twitter feed for their academic internal medicine residency program. Participants included the chief residents and categorical internal medicine house staff. RESULTS: At the year's end, the authors surveyed residents about uses and attitudes toward this initiative. Residents generally found the chief residents' tweets informative, and most residents (42/61, 69%) agreed that Twitter enhanced their overall education in residency. CONCLUSIONS: Data from this single-site intervention corroborate that Twitter can strengthen a residency program's educational mission. The program's robust following on Twitter outside of the home program also suggests a need for wider adoption of social media in graduate medical education. Improved use of data analytics and dissemination of these practices to other programs would lend additional insight into social media's role in improving residents' educational experiences.
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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 | 0.004 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".