Cross-cultural validation of the Debriefing Experience Scale: French version
Bibliographic record
Abstract
Background High-quality debriefing is critical for effective simulation-based education; reliable tools are needed to assess its quality. Such tools must be validated for use with diverse populations, including French-speaking learners, to ensure their applicability across cultural and linguistic contexts. The French version of the Debriefing Experience Scale (DES-FR) was developed to address this need. Methods A total of 396 French-speaking healthcare students completed the DES-FR. Internal consistency, test-retest reliability, confirmatory factor analysis, and measurement invariance were assessed. Results The DES-FR showed excellent internal consistency, good to excellent test-retest reliability, and strong structural validity, supporting a four-factor model. Metric invariance was achieved for the Experience scale, while partial invariance was observed for the Importance scale. Conclusions The DES-FR is a robust instrument with strong psychometric properties, suitable for assessing debriefing quality in simulation-based education among French-speaking healthcare students.
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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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".