Assessing the Assessment in Emergency Care Training
Bibliographic record
Abstract
OBJECTIVE: Each year over 1.5 million health care professionals attend emergency care courses. Despite high stakes for patients and extensive resources involved, little evidence exists on the quality of assessment. The aim of this study was to evaluate the validity and reliability of commonly used formats in assessing emergency care skills. METHODS: Residents were assessed at the end of a 2-week emergency course; a subgroup was videotaped. Psychometric analyses were conducted to assess the validity and inter-rater reliability of the assessment instrument, which included a checklist, a 9-item competency scale and a global performance scale. RESULTS: A group of 144 residents and 12 raters participated in the study; 22 residents were videotaped and re-assessed by 8 raters. The checklists showed limited validity and poor inter-rater reliability for the dimensions "correct" and "timely" (ICC = .30 and.39 resp.). The competency scale had good construct validity, consisting of a clinical and a communication subscale. The internal consistency of the (sub)scales was high (α = .93/.91/.86). The inter-rater reliability was moderate for the clinical competency subscale (.49) and the global performance scale (.50), but poor for the communication subscale (.27). A generalizability study showed that for a reliable assessment 5-13 raters are needed when using checklists, and four when using the clinical competency scale or the global performance scale. CONCLUSIONS: This study shows poor validity and reliability for assessing emergency skills with checklists but good validity and moderate reliability with clinical competency or global performance scales. Involving more raters can improve the reliability substantially. Recommendations are made to improve this high stakes skill assessment.
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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 | 0.016 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".