Evaluation of a multifaceted "Resident-as-Teacher" educational intervention to improve morning report
Bibliographic record
Abstract
BACKGROUND: Resident-led morning report is an integral part of most residency programs and is ranked among the most valuable of educational experiences. The objectives of this study were to evaluate the effect of a resident-as-teacher educational intervention on the educational and teaching experience of morning report. METHODS: All senior internal medicine residents were invited to participate in this study as teaching participants. All internal medicine residents and clerks were invited to participate as audience participants. The educational intervention included reading material, a small group session and feedback after teaching sessions. The educational and teaching experiences were rated prior to and three months after the intervention using questionnaires. RESULTS: Forty-six audience participants and 18 teaching participants completed the questionnaires. The degree to which morning report met the educational needs of the audience was higher after the educational intervention (effect size, d = 0.26, p = 0.01). The perceptions of the audience were that delivery had improved and that the sessions were less intimidating and more interactive. The perception of the teaching participants was that delivery was less stressful, but this group now reported greater difficulty in engaging the audience and less confidence in their medical knowledge. CONCLUSION: Following the educational intervention the audience's perception was that the educational experience had improved although there were mixed results for the teaching experience. When evaluating such interventions it is important to evaluate the impact on both the educational and teaching experiences as results may differ.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".