Morning Report Blog: A Web-Based Tool to Enhance Case-Based Learning
Bibliographic record
Abstract
BACKGROUND: Morning report is an interactive case-based teaching session common to internal medicine training programs across North America. DESCRIPTION: We report here on a morning report web log ("blog"), created and updated after morning report sessions by the Chief Medical Resident with pertinent clinical topics, links to journal articles, and medical images. Trainees on their internal medicine rotation were e-mailed a web link with each posting. The aim was to enhance learning on clinical topics discussed at morning report by reinforcing topics and promoting further reading. EVALUATION: The educational impact of the blog was evaluated using detailed web metrics and surveys of attendees. The intended audience spent on average more than 5 min reading the blog and viewed more than 3 pages per visit. Almost half of attendees accessed the blog after completing their internal medicine rotation. The blog was also accessed by a global audience. Trainees rated the blogs a useful learning tool and cited it to be among the top 3 educational resources accessed during their rotation. CONCLUSIONS: In summary, a morning report blog was perceived by learners to be an effective complement to case-based teaching sessions. The combination of novel web metrics and survey data allowed for a multifaceted evaluation of the educational impact of the blog.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".