Bottom-up feedback to improve clinical teaching: validation of the Swiss System for Evaluation of Teaching Qualities (SwissSETQ)
Bibliographic record
Abstract
AIMS OF THE STUDY: Clinical teaching is essential in preparing trainees for independent practice. To improve teaching quality, clinical teachers should be provided with meaningful and reliable feedback from trainees (bottom-up feedback) based on up-to-date educational concepts. For this purpose, we designed a web-based instrument, "Swiss System for Evaluation of Teaching Qualities" (SwissSETQ), building on a well-established tool (SETQsmart) and expanding it with current graduate medical education concepts. This study aimed to validate the new instrument in the field of anaesthesiology training. METHODS: Based on SETQsmart, we developed an online instrument (primarily including 34 items) with generic items to be used in all clinical disciplines. We integrated the recent educational frameworks of CanMEDS 2015 (Canadian Medical Educational Directives for Specialists), and of entrustable professional activities (EPAs). Newly included themes were "Interprofessionalism", "Patient centredness", "Patient safety", "Continuous professional development', and "Entrustment decisions". We ensured content validity by iterative discussion rounds between medical education specialists and clinical supervisors. Two think-aloud rounds with residents investigated the response process. Subsequently, the instrument was pilot-tested in the anaesthesia departments of four major teaching hospitals in Switzerland, involving 220 trainees and 120 faculty. We assessed the instrument's internal structure (to determine the factorial composition) using exploratory factor analysis, internal statistical consistency (by Cronbach's alpha as an estimate of reliability, regarding alpha >0.7 as acceptable, >0.8 as good, >0.9 as excellent), and inter-rater reliability (using generalisability theory in order to assess the minimum number of ratings necessary for a valid feedback to one single supervisor). RESULTS: Based on 185 complete ratings for 101 faculty, exploratory factor analysis revealed four factors explaining 72.3% of the variance (individual instruction 33.8%, evaluation of trainee performance 20.9%, teaching professionalism 12.8%; entrustment decisions 4.7%). Cronbach's alpha for the total score was 0.964. After factor analysis, we removed one item to arrive at 33 items for the final instrument. Generalisability studies yielded a minimum of five to six individual ratings to provide reliable feedback to one supervisor. DISCUSSION: The SwissSETQ possesses high content validity and an "excellent" internal structure for integrating up-to-date graduate medical education concepts. Thereby, the tool allows reliable bottom-up feedback by trainees to support clinical teachers in improving their teaching. Transfer to disciplines other than anaesthesiology needs to be further explored.
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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 | 0.031 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".