Bridging the gap: Comparing pediatric faculty and residents perspectives on the in-training evaluation report (ITER)
Bibliographic record
Abstract
Background The In-training evaluation report (ITER) is a reflection of the seven CanMEDS roles established by the Royal College of Physicians and Surgeons of Canada and plays a significant role in resident assessment and determining overall competency. However, unfortunately, ITERs have been shown to be inaccurate and unreliable. This unreliability has been sited due to a variety of factors including: lack of defined standards leading to subjectivity between evaluators; fragmented observation of residents and lack of timeliness in feedback delivery. As a result of the multiple challenges surrounding ITERs there has been a growing interest in examining faculty and resident attitudes toward the ITER process. Objectives To compare pediatric faculty and resident perspectives on the structure and process of the ITER to improve the ITER as an evaluation tool for Memorial University’s pediatric residency training program Methods Two separate focus groups were conducted involving both residents and faculty with preset discussion questions. Eight residents (7 female, 1 male), representing a spectrum of training years (3 PGY-1; 3 PGY-2; 2 PGY-3) and nine staff faculty (5 female, 4 male) from a variety of specialties participated. Each focus group was recorded and transcribed verbatim without identifying data. Multiple analyses of the focus group data yielded themes were sub-divided into two categories: 1. ITER Structure & Format and 2. ITER Process & Culture. Results Quotes from faculty and residents highlighted the following themes within structure and format: the importance of written feedback, contextualizing the evaluation and the importance of follow up. Within process and culture, the following themes were highlighted: engagement and timeliness, need for more observation, level of interaction, accountability and the importance of giving and receiving verbal feedback. Conclusion Residents and faculty shared many similar suggestions on how the ITER as an evaluation tool could be improved. The biggest challenge continues to be the discrepancy in the quality of feedback sought by the residents through the ITER and the faculty members ability to do so in a time effective way. Further research is needed on how residents self-assess and how this process is impacted by receiving constructive or negative feedback.
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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 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".