Enterprise Microblogging to Augment the Subinternship Clinical Learning Experience: A Proof-of-Concept Quality Improvement Study
Bibliographic record
Abstract
BACKGROUND: Although the Clerkship Directors in Internal Medicine (CDIM) has created a core subinternship curriculum, the traditional experiential subinternship may not expose students to all topics. Furthermore, academic institutions often use multiple clinical training sites for the student clerkship experience. OBJECTIVE: The objective of this study was to sustain a Web-based learning community across geographically disparate sites via enterprise microblogging to increase subintern exposure to the CDIM curriculum. METHODS: Internal medicine subinterns used Yammer, a Health Insurance Portability and Accountability Act (HIPAA)-secure enterprise microblogging platform, to post questions, images, and index conversations for searching. The subinterns were asked to submit 4 posts and participate in 4 discussions during their rotation. Faculty reinforced key points, answered questions, and monitored HIPAA compliance. RESULTS: In total, 56 medical students rotated on an internal medicine subinternship from July 2014 to June 2016. Of them, 84% returned the postrotation survey. Over the first 3 months, 100% of CDIM curriculum topics were covered. Compared with the pilot year, the scale-up year demonstrated a significant increase in the number of students with >10 posts (scale-up year 49% vs pilot year 19%; P=.03) and perceived educational experience (58% scale-up year vs 14% pilot year; P=.006). Few students (6%) noted privacy concerns, but fewer students in the scale-up year found Yammer to be a safe learning environment. CONCLUSIONS: Supplementing the subinternship clinical experience with an enterprise microblogging platform increased subinternship exposure to required curricular topics and was well received. Future work should address concerns about safe learning environment.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".