The Use of Formative Assessment in Postgraduate Urology Training: A Systematic Review
Bibliographic record
Abstract
Formative assessment is an essential component of surgical training. However, it is not usually a mandatory component in postgraduate curricula. The purpose of this study is to identify and evaluate how formative assessments are integrated into postgraduate urology training in programs across the globe. This study consisted of a systemic review to see how formative assessments are being implemented in various urology programs globally. A total of 427 articles were identified for the literature review. Of these, only 10 were included and critically appraised. These studies explored various techniques for exploration of formative assessments in urology training programs, which included established tools, such as portfolio reviews, and direct observations of procedure skills (DOPS); novel tools, including the Dutch urology practical skills (D-UPS) program and Ottawa surgical competency operating room evaluation (O-SCORE); and curricular models. Nine of the 10 articles favored their potential utility in formative assessments. Current literature involving formative assessments in postgraduate urology programs is scarce, and available resources have a high heterogeneity between them. More structured formative assessments need to be incorporated into surgical training programs, and affiliated training institutions should be encouraged to integrate them into their curricula.
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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 | 0.026 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".