Does a Structured Formative Feedback System Improve Learner Perception of the Obstetrics and Gynecology Clerkship? [16H]
Bibliographic record
Abstract
INTRODUCTION: Medical students are more likely to pursue specialty training in fields they perceived positively during their training. This can be influenced by their satisfaction with feedback during a rotation. We investigated whether a structured formative feedback system would improve learner perception of the Obstetrics and Gynecology clerkship. METHODS: This observational cohort study included medical students from the Class of 2017 (control cohort) and 2018 (intervention cohort). Institutional ethics board approval was obtained. A formative feedback system was implemented during Obstetrics and Gynecology rotations for the Class of 2018. It consisted of a set of electronic forms asking for strengths and requirements for improvement in the student's assessment of a patient with a particular presenting complaint. Students were invited to complete a survey about their overall rotation experience during their final year of training using Likert Scales. The primary outcome was the proportion of students who reported a positive experience during their rotation. Chi-squared analysis was used to compare the proportion of positive responses between each group. RESULTS: 190/286 (66.4%) students responded to the survey. 72/103 (70.6%) and 65/87 (74.7%) students reported a positive experience on their rotation (good, very good or excellent) in the control and intervention cohorts, respectively (P=.5). 27/83 (31.0%) of students felt the system positively influenced the rotation. CONCLUSION: Implementation of this structured formative feedback system did not improve learner perception of the rotation. Further research is needed to determine if mechanisms that ensure students receive specific and actionable feedback would affect this outcome during an Obstetrics and Gynecology rotation.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".