A needs assessment of surgical residents as teachers.
Bibliographic record
Abstract
OBJECTIVE: To determine the needs of surgical residents as teachers of clinical clerks. DESIGN: A needs assessment survey. SETTING: Department of Surgery, University of Toronto. PARTICIPANTS: Clinical clerks and surgical residents and staff surgeons. METHODS: Three stakeholder groups were defined: staff surgeons, surgical residents and clinical clerks. Focus-group sessions using the nominal group technique identified key issues from the perspectives of clerks and residents. Resulting information was used to develop needs assessment surveys, which were administered to 170 clinical clerks and 190 surgical residents. Faculty viewpoints were assessed with semi-structured interviews. Triangulation of these 3 data sources provided a balanced approach to identifying the needs of surgical residents as teachers. RESULTS: Response rates were 64% for clinical clerks and 66% for surgical residents. Five staff surgeons were interviewed. Consensus was noted among the stakeholder groups regarding the importance of staff surgeon role modelling and feedback, resident attitude, time management, knowledge of clerks' formal learning objectives, and appropriate times and locations for teaching. Discrepancies included a significant difference in opinion regarding the residents' capacity to address clerks' individual learning needs and to foster good team relationships. Residents indicated that they did not receive regular feedback regarding their teaching and that staff did not place an emphasis on their teaching role. CONCLUSIONS: This study has, from a multi-source perspective, assessed the needs of surgical residents as teachers. These needs include enhancing residents' education regarding how and what to teach medical students on a surgical rotation, and a need for staff surgeons to increase feedback to residents regarding their teaching.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".