The Ottawa Emergency Department Shift Observation Tool (O‐EDShOT): A New Tool for Assessing Resident Competence in the Emergency Department
Bibliographic record
Abstract
OBJECTIVES: The outcome of emergency medicine (EM) training is to produce physicians who can competently run an emergency department (ED) shift. However, there are few tools with supporting validity evidence specifically designed to assess multiple key competencies across an entire shift. The investigators developed and gathered validity evidence for a novel entrustment-based tool to assess a resident's ability to safely run an ED shift. METHODS: Through a nominal group technique, local and national stakeholders identified dimensions of performance that are reflective of a competent ED physician and are required to safely manage an ED shift. These were included as items in the Ottawa Emergency Department Shift Observation Tool (O-EDShOT), and each item was scored using an entrustment-based rating scale. The tool was implemented in 2018 at the University of Ottawa Department of Emergency Medicine, and quantitative data and qualitative feedback were collected over 6 months. RESULTS: A total of 1,141 forms were completed by 78 physicians for 45 residents. An analysis of variance demonstrated an effect of training level with statistically significant increases in mean O-EDShOT scores with each subsequent postgraduate year (p < 0.001). Scores did not vary by ED treatment area. Residents rated as able to safely run the shift had significantly higher mean ± SD scores (4.8 ± 0.3) than those rated as not able (3.8 ± 0.6; p < 0.001). Faculty and residents reported that the tool was feasible to use and facilitated actionable feedback aimed at progression toward independent practice. CONCLUSIONS: The O-EDShOT successfully discriminated between trainees of different levels regardless of ED treatment area. Multiple sources of validity evidence support the O-EDShOT as a tool to assess a resident's ability to safely run an ED shift. It can serve as a stimulus for daily observation and feedback making it practical to use within an EM residency program.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".