Transition to practice: Evaluating the need for formal training in supervision and assessment among senior emergency medicine residents and new to practice emergency physicians
Bibliographic record
Abstract
OBJECTIVES: Emergency medicine residents may be transitioning to practice with minimal training on how to supervise and assess trainees. Our study sought to examine: 1) physician comfort with supervision and assessment, 2) what the current training gaps are within these competencies, and 3) what barriers or enablers might exist in implementing curricular improvements. METHODS: Qualitative data were collected in two phases through individual interviews from September 2016 to November 2017, at the University of Toronto and McMaster University after receiving ethics approval from both sites. Eligible participants were final year emergency medicine residents, residents pursuing an enhanced skills program in emergency medicine, and attendings within their first 3 years of practice. A semi-structured interview guide was developed and refined after phase one, to reflect content identified in the first set of interviews. All interviews were recorded, transcribed, coded, and collapsed into themes. Data analysis was guided by constructivist grounded theory. RESULTS: A thematic analysis revealed five themes: 1) Supervision and assessment skills were acquired passively through modelling, 2) the training available in these areas is variably used, creating a diversity of comfort levels, 3) competing priorities in the emergency department represent significant barriers to improving supervision and assessment; 4) providing negative feedback is difficult and often avoided; and 5) competence by design will act as an impetus for formal curriculum development in these areas. CONCLUSIONS: As programs transition to competence by design, there will be a need for formal training in supervision and assessment, with a focus on negative feedback, to achieve a standardized level of competence among emergency physicians.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".