Development and Implementation of a Web-Based Learning Environment for an Inpatient Internal Medicine Team: Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: The notion of anytime, anyplace communication is characteristic of the current generation of learners. Such communications have facilitated the growth and integration of a blended or hybrid learning platform in multiple educational settings. However, there are limited reports on the use of an anytime, anyplace communication platform in clinical inpatient medical education. OBJECTIVE: The setting of a high-demand inpatient clinical rotation is ideal for the use of collaborative software, and this integration is expected to positively influence medical education. The purpose of this study is to evaluate medical students' and residents' educational experiences with incorporating a simple, web-based content management and file sharing platform into an internal medicine inpatient rotation. METHODS: During an inpatient internal medicine rotation, faculty and learners jointly used collaborative software for educational purposes, and a postrotation survey tool was used to measure the educational influence of the software. RESULTS: Based on the results of the postrotation survey, the integration of a collaborative software application during clinical rotations improved the learning experience. Learning climate, the communication of rotation goals, and self-directed learning all scored favorably, but feedback from the survey participants was mixed. The learners enthusiastically accepted the practical use of this tool for both communication and information sharing. CONCLUSIONS: This generation of learners is accustomed to frequent electronic communication. Based on our survey, these learners appear to be highly receptive to this web-based intervention design for improving clinical education during active patient care. Adding effective blended learning features to a traditional clinical setting is achievable.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".