A new tool for assessing short debriefings after immersive simulation: validity of the SHORT scale
Bibliographic record
Abstract
BACKGROUND: Simulation is being increasingly used worldwide in healthcare education. However, it is costly both in terms of finances and human resources. As a consequence, several institutions have designed programs offering several short immersive simulation sessions, each followed by short debriefings. Although debriefing is recommended, no tool exists to assess appropriateness of short debriefings after such simulation sessions. We have developed the Simulation in Healthcare retrOaction Rating Tool (SHORT) to assess short debriefings, and provide some validity evidence for its use. METHODS: We designed this scale based on our experience and previously published instruments, and tested it by assessing short debriefings of simulation sessions offered to emergency medicine residents at Laval University (Canada) from 2015 to 2016. Analysis of its reliability and validity was done using Standards for educational and psychological testing. Generalizability theory was used for testing internal structure evidence for validity. RESULTS: Two raters independently assessed 22 filmed short debriefings. Mean debriefing length was 10:35 (min 7:21; max 14:32). Calculated generalizability (reliability) coefficients are φ = 0.80 and φ-λ3 = 0.82. The generalizability coefficient for a single rater assessing three debriefings is φ = 0.84. CONCLUSIONS: The G study shows a high generalizability coefficient (φ ≥ 0.80), which demonstrates a high reliability. The response process evidence for validity provides evidence that no errors were associated with using the instrument. Further studies should be done to demonstrate validity of the English version of the instrument and to validate its use by novice raters trained in the use of the SHORT.
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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 | 0.021 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".