Reflective Practice in Anesthesia Clinical Teaching
Bibliographic record
Abstract
Background: Reflective practice is an essential aspect of knowledge generation for professional practice. By reflecting on action, professionals learn to improve their practices. Through processes of reflection, practitioners participate in a dialogue between theory and practice. Even though reflective practice is an important approach for learning from experience, its place remains unclear in anesthesia clinical education as well as anesthesia practice in a broad sense. Aim: The aim of this paper was to examine the affordances of reflective practice in anesthesia clinical education. Methods: Two cases, illustrating critical incidents in the anesthesia clinical teaching environment, were examined to consider how incorporating reflective practice into clinical education can advance knowledge generation in the field. Findings: The two cases studies show how reflective practice can contribute to experiential learning, particularly through reflection on critical incidents. Conclusion: Reflective practice can help bridge the gap between theoretical knowledge and practice in anesthesia education and practice.
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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 | 0.051 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".