Physician engagement in regularly scheduled rounds
Bibliographic record
Abstract
Background: Physician participation in regularly scheduled series (RSS), also known as grand rounds, was explored with a particular focus on physician perceptions about the elements that affected their engagement in RSS and the unanticipated benefits to RSS. Methods: A qualitative study using semi-structured interviews and thematic analysis examined physicians’ perception of their knowledge and educational needs and the factors that contributed to engagement in their local hospital RSS. Results: Physician engagement in RSS was affected by four major themes: Features that Affect the RSS’ Quality; Collegial Interactions; Perceived Outcomes of RSS; and Barriers to participation in RSS. Features that Affect RSS’ Quality were specific modifiable features that impacted the perceived quality of the RSS. Collegial Interactions were interactions that occurred between colleagues directly or indirectly as a result of attending RSS. Outcomes of RSS were specific outcome measures used in RSS sessions. Barriers were seen as reasons why physicians were unwilling or unable to participate in RSS. All of the elements identified within the four themes contributed to the development of physician engagement. Physicians also identified changes directly and indirectly due to RSS. Discussion: Specific features of RSS result in enhanced physician engagement. There are benefits that may not be accounted for in continuing medical education (CME) outcome study designs
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".