Mixed Messages or Miscommunication? Investigating the Relationship Between Assessors’ Workplace-Based Assessment Scores and Written Comments
Bibliographic record
Abstract
PURPOSE: The shift toward broader, programmatic assessment has revolutionized the approaches that many take in assessing medical competence. To understand the association between quantitative and qualitative evaluations, the authors explored the relationships that exist among assessors' checklist scores, task ratings, global ratings, and written comments. METHOD: The authors collected and analyzed, using regression analyses, data from the McMaster Modular Assessment Program. The data were from emergency medicine residents in their first or second year of postgraduate training from 2012 through 2014. Additionally, using content analysis, the authors analyzed narrative comments corresponding to the "done" and "done, but needs attention" checklist score options. RESULTS: The regression analyses revealed that the task ratings, provided by faculty assessors, are associated with the use of the "done, but needs attention" checklist score option. Analyses also identified that the "done, but needs attention" option is associated with a narrative comment that is balanced, providing both strengths and areas for improvement. Analysis of qualitative comments revealed differences in the type of comments provided to higher- and lower-performing residents. CONCLUSIONS: This study highlights some of the relationships that exist among checklist scores, rating scales, and written comments. The findings highlight that task ratings are associated with checklist options while global ratings are not. Furthermore, analysis of written comments supports the notion of a "hidden code" used to communicate assessors' evaluation of medical competence, especially when communicating areas for improvement or concern. This study has implications for how individuals should interpret information obtained from qualitative assessments.
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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 | 0.088 | 0.602 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".