Numbers Encapsulate, Words Elaborate: Toward the Best Use of Comments for Assessment and Feedback on Entrustment Ratings
Bibliographic record
Abstract
The adoption of entrustment ratings in medical education is based on a seemingly simple premise: to align workplace-based supervision with resident assessment. Yet it has been difficult to operationalize this concept. Entrustment rating forms combine numeric scales with comments and are embedded in a programmatic assessment framework, which encourages the collection of a large quantity of data. The implicit assumption that more is better has led to an untamable volume of data that competency committees must grapple with. In this article, the authors explore the roles of numbers and words on entrustment rating forms, focusing on the intended and optimal use(s) of each, with a focus on the words. They also unpack the problematic issue of dual-purposing words for both assessment and feedback. Words have enormous potential to elaborate, to contextualize, and to instruct; to realize this potential, educators must be crystal clear about their use. The authors set forth a number of possible ways to reconcile these tensions by more explicitly aligning words to purpose. For example, educators could focus written comments solely on assessment; create assessment encounters distinct from feedback encounters; or use different words collected from the same encounter to serve distinct feedback and assessment purposes. Finally, the authors address the tyranny of documentation created by programmatic assessment and urge caution in yielding to the temptation to reduce words to numbers to make them manageable. Instead, they encourage educators to preserve some educational encounters purely for feedback, and to consider that not all words need to become data.
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 | 0.165 | 0.511 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".