Academic half days, noon conferences and classroom-based education in postgraduate medical education: a scoping review
Bibliographic record
Abstract
BACKGROUND: Classroom-based education (CBE) is ubiquitous in postgraduate medical education (PGME), but to date no studies have synthesized the literature on the topic. We conducted a scoping review focusing on academic half days and noon conferences. METHODS: We searched 4 databases (MEDLINE [OVID], Embase [OVID], ERIC [EBSCO] and Web of Science) from inception to December 2021, performed reference and citation harvesting, and applied predetermined inclusion and exclusion criteria to our screening. We used 2 frameworks for the analysis: "experiences, trajectories and reifications" and "description, justification and clarification." RESULTS: We included 90 studies, of which 55 focused on resident experiences, 29 on trajectories and 6 on reification. We classified 44 studies as "description," 38 as "justification" and 8 as "clarification." In the description studies, 12 compared academic half days with noon conferences, 23 described specific teaching topics, and 9 focused on resources needed for CBE. Justification studies examined the effects of CBE on outcomes, such as examination scores (17) and use of teaching strategies in team-based learning, principles of adult learning and e-learning (15). Of the 8 clarification studies, topics included the role of CBE in PGME, stakeholder perspectives and transfer of knowledge between classroom and workplace. INTERPRETATION: Much of the existing literature is either a description of various aspects of CBE or justification of particular teaching strategies. Few studies exist on how and why CBE works; future studies should aim to clarify how CBE facilitates resident learning within the sociocultural framework of PGME.
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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 | 0.017 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".