Development and Validity of a Simulation Program for Assessment of Clinical Teaching Skills
Bibliographic record
Abstract
Abstract Background Teaching competence is expected of all intensivists, yet experts rarely supervise or assess trainees’ teaching skills. Simulation offers an attractive solution. Objective Develop and validate a simulation-based assessment of clinical teaching skills in pediatric critical care medicine (CCM). Methods Participants were 128 pediatric CCM trainees, registered nurses, and respiratory therapists. Medical education experts used literature review and consensus to design three scenarios to assess teaching skills. Scenarios were piloted before use, and raters were trained. Teams completed one of three teaching scenarios, followed by a communication scenario. Raters were faculty members and trainees. Evidence for validity was collected and analyzed using Messick’s unifying framework under the following domains: content, response processes, internal structure, relationship to other variables, and consequences of the assessment. Results The scenarios and assessment tools were designed to capture the characteristics of a good teacher as described in the literature. Raters provided feedback that the tools were easy to use. Internal consistency of the scores measured by Cronbach’s α was high. Rater agreement measured by interclass correlation was moderate for one of three scenarios. The relationship to other variables was investigated by correlating teaching scores with communication. Pearson’s correlation was moderate for two of three scenarios. Consequences evidence was gathered using a retrospective self-assessed learning gain before versus after the training, which was significant for all scenarios. Conclusion We developed a three-station simulation program for the assessment of teaching skills in pediatric CCM. The validity evidence collected is moderate, which indicates that it is effective for training and feedback on teaching skills.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".