Should Medical School Faculty See Assessments of Students Made by Previous Teachers?
Bibliographic record
Abstract
Whether medical school faculty should be provided with assessments of students made by previous teachers remains controversial. To document which schools have implemented policies that address this issue and to characterize the specific features of these policies, in 1998 the authors conducted a direct mail survey of deans of student affairs and medical education at 144 medical schools in the United States, Canada, and Puerto Rico. Replies were received from 129 (90%) of the 144 medical schools. Of those schools, 72 (56%) reported having policies that address this issue. The policies permit the sharing of information in 38 (53%) of the 72 schools that had policies; therefore, at the time of this study, 29% of the 129 medical schools that responded to the survey had a policy that permits the sharing of assessment information. The policies permit the sharing of information related to problems with academic performance (35%), professional conduct (35%), physical health (25%), and miscellaneous circumstances, such as learning disability (5%). Information may be shared with clerkship coordinators (44%), course directors (35%), faculty mentors (11%), clinical faculty supervisors (8%), and resident supervisors (3%). The findings show that there is considerable diversity in the format and content of policies that address the issue of whether medical school faculty should be provided with information about students' assessments made by previous teachers. The authors explain why policies that require the provision of such information are helpful to medical school faculty, and offer recommendations based on the survey findings.
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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 | 0.015 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".