Idiosyncrasy in Assessment Comments: Do Faculty Have Distinct Writing Styles When Completing In-Training Evaluation Reports?
Bibliographic record
Abstract
PURPOSE: Written comments are gaining traction as robust sources of assessment data. Compared with the structure of numeric scales, what faculty choose to write is ad hoc, leading to idiosyncratic differences in what is recorded. This study offers exploration of what aspects of writing styles are determined by the faculty offering comment and what aspects are determined by the trainee being commented upon. METHOD: The authors compiled in-training evaluation report comment data, generated from 2012 to 2015 by 4 large North American Internal Medicine training programs. The Linguistic Index and Word Count (LIWC) was used to categorize and quantify the language contained. Generalizability theory was used to determine whether faculty could be reliably discriminated from one another based on writing style. Correlations and ANOVAs were used to determine what styles were related to faculty or trainee demographics. RESULTS: Datasets contained 23-142 faculty who provided 549-2,666 assessments on 161-989 trainees. Faculty could easily be discriminated from one another using a variety of LIWC metrics including word count, words per sentence, and the use of "clout" words. These patterns appeared person specific and did not reflect demographic factors such as gender or rank. These metrics were similarly not consistently associated with trainee factors such as postgraduate year or gender. CONCLUSIONS: Faculty seem to have detectable writing styles that are relatively stable across the trainees they assess, which may represent an under-recognized source of construct irrelevance. If written comments are to meaningfully contribute to decision making, we need to understand and account for idiosyncratic writing styles.
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 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 | 0.047 | 0.310 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".