Are we generating more assessments without added value? Surgical trainees’ perceptions of and receptiveness to cross-specialty assessment
Bibliographic record
Abstract
INTRODUCTION: Competency-based medical education (CBME) hinges on robust assessment. However, integrating regular workplace-based assessment within demanding and sometimes chaotic clinical environments remains challenging. Many faculty lack assessment expertise, and some programs lack the infrastructure and faculty numbers to fulfill CBME's mandate. Recognizing this, we designed and implemented an assessment innovation that trains and deploys a cadre of faculty to assess in specialties outside their own. Specifically, we explored trainees' perceptions of and receptiveness to this novel assessment approach. METHODS: Within Western University's Surgical Foundations program, 27 PGY‑1 trainees were formatively assessed by trained non-surgeons on a basic laparoscopic surgical skill. These assessments did not impact trainees' progression. Four focus groups were conducted to gauge residents' sentiments about the experience of cross-specialty assessment. Data were then analyzed using a thematic analysis approach. RESULTS: While a few trainees found the experience motivating, more often trainees questioned the feedback they received and the practicality of this assessment approach to advance their procedural skill acquisition. What trainees wanted were strategies for improvement, not merely an assessment of performance. DISCUSSION: Trainees' trepidation at the idea of using outside assessors to meet increased assessment demands appeared grounded in their expectations for assessment. What trainees appeared to desire was a coach-someone who could break their performance into its critical individual components-as opposed to an assessor whose role was limited to scoring their performance. Understanding trainees' receptivity to new assessment approaches is crucial; otherwise training programs run the risk of generating more assessments without added value.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".