How Teachers Adapt Their Cognitive Strategies When Using Entrustment Scales
Bibliographic record
Abstract
PURPOSE: Rater cognition is a field of study focused on individual cognitive processes used by medical teachers when completing assessments. Much has been written about the role of rater cognition in the use of traditional assessment scales. Entrustment scales (ES) are becoming the tool of choice for workplace-based assessments. It is not known how moving to an entrustment framework may cause teachers to adapt their cognitive rating strategies. This study aimed to explore this gap by asking teachers to describe their thinking when making rating decisions using a validated ES. METHOD: Using purposive sampling, family medicine teachers supervising obstetrical care were invited to participate in cognitive interviews. Teachers were interviewed between December 2018 and March 2019 using retrospective verbal protocol analysis. They were asked to describe their experiences of rating residents in the last 6 months using new ES. Constructivist grounded theory guided data collection and analysis. Interviews were recorded, transcribed, and analyzed iteratively. A constant comparative approach was used to code and analyze the data until consensus was reached regarding emerging themes. RESULTS: There was variability in how teachers used the ES. Faculty describe several ways in which they ultimately navigated the tool to say what they wanted to say. Four key themes emerged: (1) teachers interpreted the anchors differently based on their cognitive framework, (2) teachers differed in how they were able to cognitively shift away from traditional rating scales, (3) teachers struggled to limit assessments to a report on observed behavior, and (4) teachers contextualized their ratings. CONCLUSIONS: Variability in teachers' interpretation of learner performance persists in entrustment frameworks. Rater's individual cognitive strategies and how they observe, process, and integrate their thoughts into assessments form part of a rich picture of learner progress. These insights can be harnessed to contribute to decisions regarding readiness for unsupervised practice.
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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 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".