Completing a Quality Evaluation Report — What Clinical Supervisors Need to Know — A Faculty Development Workshop
Bibliographic record
Abstract
Abstract Introduction For medical students and residents completing the clinical portion of their training, clinical supervisors complete a large part of their learning assessment through the use of in-training evaluation (ITE). The supervisors document their evaluation on an in-training evaluation report (ITER). Physicians who supervise medical trainees have indicated that they want faculty development (FD) programs to help them improve their ability to complete the in-training evaluation reports that they are required to provide to trainees and their program directors. Based on both perceived and observed needs we chose to develop a FD workshop to teach clinical supervisors how to complete better quality ITERs. Methods The workshop can been given using different timelines. The workshop outline document describes the suggested timelines depending on if you want to plan a 1.5-hour, 2-hour, 2.5-hour or 3-hour workshop. The main changes are to the introductions and the amount of time for discussion and practice. Results This workshop has been evaluated as part of a multi-site research study. Two types of evaluation were used. First, a validated CME rating scale was used to assess participant satisfaction with the workshop. Second, we compared the quality of clinical supervisors' completed evaluation reports pre-workshop to those that they completed in the six months following the workshop. The quality of the evaluation reports was assessed using a validated tool, the completed clinical evaluation report rating (CCERR). Participants were highly satisfied with our workshop. We found a significant improvement in evaluation report quality following the workshop. Discussion This workshop focuses solely on the completion of evaluation reports. Many other barriers to accurately reporting training performance such as a lack of time for observation, limited trainee contact, etc. exist. Users of this workshop may want to incorporate it into a broader faculty development program that addresses more aspects of trainee assessment.
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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 | 0.053 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".