Bibliographic record
Abstract
We thank Bohler and colleagues for their interest in our article and for pointing out yet another threat to the utility of teaching evaluations (TEs). We agree that there can be a high burden on students for filling out course evaluations and teacher assessments, and that forcing people to submit feedback may not result in the most useful feedback. One review article summarized the literature and highlighted many useful strategies to improve course evaluation,1 including randomly sampling groups of students for each lecture or course in order to decrease the burden.2 The challenge of assessment burden exists for all groups. We calculated that for an average team size of 4 residents, an internal medicine staff physician who attends for 4 months per year may be required to complete up to 128 assessments of entrustable professional activities and 16 in-training evaluation reports, plus additional medical student assessments. Strategies that balance and optimize the quantity and quality of feedback are needed to improve processes for all. Further, we agree with the authors that anonymity certainly exacerbates all of the issues noted and is one of the key threats to having TEs function as feedback. Feedback is thought to be best when it is specific and contextual, which is missing when the recipient does not know who wrote the feedback or what it might be referring to.3 It makes it nearly impossible for the recipient to know what or how to improve. At least one group of clinicians has instituted nonanonymous feedback, which is something others may wish to explore.4 In the meantime, we should continue to seek the input of learners about their teachers. However, we must recognize the limits of what students are able to meaningfully assess, and situate these assessments as but one element of a broader appraisal of teaching and clinical supervision. The time has come to stop relying on learners as the sole source of feedback for teachers. Shiphra Ginsburg, MD, MEd, PhD Professor of medicine, Department of Medicine, Sinai Health System and Temerty Faculty of Medicine, University of Toronto, and scientist, Wilson Centre for Research in Education, University of Toronto, Toronto, Ontario, Canada, and Canada Research Chair in Health Professions Education; ORCID: http://orcid.org/0000-0002-4595-6650 Lynfa Stroud, MD, MEd Associate professor, Department of Medicine and Sunnybrook Health Sciences Centre and Temerty Faculty of Medicine, University of Toronto, and education researcher, Wilson Centre for Research in Education, University of Toronto, Toronto, Ontario, Canada
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.042 | 0.051 |
| Insufficient payload (model declined to judge) | 0.013 | 0.015 |
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".