Evaluating “ED STAT!”: A Novel and Effective Faculty Development Program to Improve Emergency Department Teaching
Bibliographic record
Abstract
OBJECTIVES: Effective clinical teaching in emergency departments (EDs) presents unique challenges. No validated approaches to enhancing ED teaching have been reported. The authors evaluated the effectiveness of a novel one-day evidence-based, skills-oriented faculty development course tailored to ED teachers (ED STAT!). METHODS: The authors invited all inaugural course registrants to participate in this program evaluation study. The authors assessed participants' knowledge change and perceived change in teaching behavior using a multiple-choice and short-answer question examination, a teaching behaviors questionnaire, and a survey for satisfaction. Data were gathered before, immediately after, and one month after the course. Mean scores were compared using the Wilcoxon signed rank test, and qualitative results were analyzed via a grounded theory approach. RESULTS: Thirty-one individuals from a variety of academic and community EDs completed the May 2005 course; 28 participated in the pre-evaluation and postevaluation, and 22 participated in the one-month postevaluation. Multiple-choice scores increased from pre-evaluation to one-month postcourse by 15.1% (p < 0.001, effect size large: d = 1.53). Short-answer scores increased by 17.2% (p = 0.001, effect size large: d = 0.90). After one month, 55% of participants reported an increased amount of teaching, 86% perceived this teaching to be of a greater quality, and 82% had shared new strategies with colleagues. The course would be recommended to a colleague by 96.3% of respondents. CONCLUSIONS: ED STAT! improves participants' knowledge about ED-specific teaching strategies, and this improvement is maintained at one month. Participants reported high satisfaction and a positive effect on teaching behavior.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".