A scoping review of virtual morning report and outcomes in Canada and the United States
Bibliographic record
Abstract
Abstract Purpose To describe the current landscape of virtual morning report (VMR) in medical residency education including its varying formats, methods, and associated effectiveness on learning and clinical outcomes. Methods The authors conducted a scoping review using the Arksey and O’Malley methodology. They searched Embase, OvidMEDLINE, Google Scholar, and PubMed between January 1, 1991 to April 15, 2022. Articles written in English on virtual morning report and virtual case-based teaching in medical residency programs were captured. Two authors independently screened articles using the inclusion criteria. Using a snowball approach, further citations were identified from included references. Two authors performed data extraction including outcomes using the Kirkpatrick model. We conducted thematic analysis using an iterative process. Results A total of 401 citations were screened for eligibility and we included 40 articles. The number of published studies per year on VMR increased since the COVID-19 pandemic. Most studies used online case-based modules (n=20; 50.0%) or videoconferencing (n=12; 30.0%). The majority of studies described improved confidence with clinical reasoning, easy access, and preference for chatboxes/polls for engagement (Kirpatrick level 1). Nineteen studies demonstrated improved knowledge acquisition with pre-and post-test scores (Kirkpatrick level 2). Behaviour changes (Kirkpatrick level 3) included improved screening tests and medication prescribing. There were no studies on clinical outcomes (Kirkpatrick level 4). Thematic analyses revealed that VMR increased clinical reasoning, efficiently used technology, provided an inclusive environment for diverse learners, but reduced peer engagement and bedside teaching. Conclusion Virtual morning report has a positive impact on learner confidence, knowledge, inclusivity, accessibility, and behaviour change. Future research is needed to explore the impact on patient outcomes as well as identify strategies for peer engagement and social interaction.
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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 | 0.031 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.040 | 0.053 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".