Embracing informed learner self-assessment during debriefing: the art of plus-delta
Bibliographic record
Abstract
The healthcare simulation field has no shortage of debriefing options. Some demand considerable skill which serves as a barrier to more widespread implementation. The plus-delta approach to debriefing offers the advantages of conceptual simplicity and ease of implementation. Importantly, plus-delta promotes learners' capacity for a self-assessment, a skill vital for safe clinical practice and yet a notorious deficiency in professional practice. The plus-delta approach confers the benefits of promoting uptake of debriefing in time-limited settings by educators with both fundamental but also advanced skills, and enhancing essential capacity for critical self-assessment informed by objective performance feedback. In this paper, we describe the role of plus-delta in debriefing, provide guidance for incorporating informed learner self-assessment into debriefings, and highlight four opportunities for improving the art of the plus delta: (a) exploring the big picture vs. specific performance issues, (b) choosing between single vs. double-barreled questions, (c) unpacking positive performance, and (d) managing perception mismatches.
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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 | 0.090 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".