Using In-Training Evaluation Report (ITER) Qualitative Comments to Assess Medical Students and Residents: A Systematic Review
Bibliographic record
Abstract
PURPOSE: In-training evaluation reports (ITERs) constitute an integral component of medical student and postgraduate physician trainee (resident) assessment. ITER narrative comments have received less attention than the numeric scores. The authors sought both to determine what validity evidence informs the use of narrative comments from ITERs for assessing medical students and residents and to identify evidence gaps. METHOD: Reviewers searched for relevant English-language studies in MEDLINE, EMBASE, Scopus, and ERIC (last search June 5, 2015), and in reference lists and author files. They included all original studies that evaluated ITERs for qualitative assessment of medical students and residents. Working in duplicate, they selected articles for inclusion, evaluated quality, and abstracted information on validity evidence using Kane's framework (inferences of scoring, generalization, extrapolation, and implications). RESULTS: Of 777 potential articles, 22 met inclusion criteria. The scoring inference is supported by studies showing that rich narratives are possible, that changing the prompt can stimulate more robust narratives, and that comments vary by context. Generalization is supported by studies showing that narratives reach thematic saturation and that analysts make consistent judgments. Extrapolation is supported by favorable relationships between ITER narratives and numeric scores from ITERs and non-ITER performance measures, and by studies confirming that narratives reflect constructs deemed important in clinical work. Evidence supporting implications is scant. CONCLUSIONS: The use of ITER narratives for trainee assessment is generally supported, except that evidence is lacking for implications and decisions. Future research should seek to confirm implicit assumptions and evaluate the impact of decisions.
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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 | 0.187 | 0.484 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 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".