The More Things Change, the More They Stay the Same
Bibliographic record
Abstract
Despite increasing emphasis on the development and implementation of Residents-as-Teachers programs, resident perspectives on their roles as teachers have rarely been explored. This paper explores trends across 7 years of data collected from resident leaders across North American orthopaedic residency programs. Methods: Data were collected during the American Orthopaedic Association's resident-only engagement activity known as the C. McCollister Evarts Resident Leadership Forum (RLF). A total of 997 of 1,050 RLF participants responded from 2015 to 2021. Results: Residents perceived themselves as having a strong influence on medical students' education more so than any other teacher. However, less than one third are provided with any formal instruction from their institutions to support their teaching, and nearly 3 quarters of participants felt there was room for improvement in their teaching effectiveness. For the most part, resident perspectives have stayed relatively consistent over time. Discussion: Residents are keen and willing to invest time into developing their teaching effectiveness. There may be a need for improved advocacy to program directors to increase the presence and availability of formal Residents-as-Teachers programs to support residents in their roles as teachers. We offer suggestions for the composition of such programs for consideration.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".