Pediatric faculty and residents’ perspectives on In-Training Evaluation Reports (ITERs)
Bibliographic record
Abstract
BACKGROUND: In-training evaluation reports (ITERs) are used by over 90% of postgraduate medical training programs in Canada for resident assessment. Our study examined the perspectives of faculty and residents in one pediatric program as a means to improve the ITER as an evaluation tool. METHOD: Two separate focus groups were conducted, one with eight pediatric residents and one with nine clinical faculty within the pediatrics program of Memorial University's Faculty of Medicine to discuss their perceptions of, and suggestions for improving, the use of ITERs. RESULTS: Residents and faculty shared many similar suggestions for improving the ITER as an evaluation tool. Both the faculty and residents emphasized the importance of written feedback, contextualizing the evaluation and timely follow-up. The biggest challenge appears to be the discrepancy in the quality of feedback sought by the residents and the faculty members' ability to do so in a time effective manner. Others concerns related to the need for better engagement in setting rotation objectives and more direct observation by the faculty member completing the ITER. CONCLUSIONS: The ITER is a useful tool in resident evaluations, but a number of issues relating to its actual use could improve the quality of feedback which residents receive.
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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 | 0.038 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".