In‐training evaluations: developing an automated screening tool to measure report quality
Bibliographic record
Abstract
OBJECTIVES: In-training evaluation (ITE) is used to assess resident competencies in clinical settings. This assessment is documented on an evaluation report (In-Training Evaluation Report [ITER]). Unfortunately, the quality of these reports can be questionable. Therefore, training programmes to improve report quality are common. The Completed Clinical Evaluation Report Rating (CCERR) was developed to assess completed report quality and has been shown to do so in a reliable manner, thus enabling the evaluation of these programmes. The CCERR is a resource-intensive instrument, which may limit its use. The purpose of this study was to create a screening measure (Proxy-CCERR) that can predict the CCERR outcome in a less resource-intensive manner. METHODS: Using multiple regression, the authors analysed a dataset of 269 ITERs to create a model that can predict the associated CCERR scores. The resulting predictive model was tested on the CCERR scores for an additional sample of 300 ITERs. RESULTS: The quality of an ITER, as measured by the CCERR, can be predicted using a model involving only three variables (R(2) = 0.61). The predictive variables included the total number of words in the comments, the variability of the ratings and the proportion of comment boxes completed on the form. CONCLUSIONS: It is possible to model CCERR scores in a highly predictive manner. The predictive variables can be easily extracted in an automated process. Because this model is less resource-intensive than the CCERR, it makes it possible to provide feedback from ITER training programmes to large groups of supervisors and institutions, and even to create automated feedback systems using Proxy-CCERR scores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".