Realizing One’s Own Subjectivity: Assessors’ Perceptions of the Influence of Training on Their Conduct of Workplace-Based Assessments
Bibliographic record
Abstract
PURPOSE: Assessor training is essential for defensible assessments of physician performance, yet research on the effectiveness of training programs for promoting assessor consistency has produced mixed results. This study explored assessors' perceptions of the influence of training and assessment tools on their conduct of workplace-based assessments of physicians. METHOD: In 2017, the authors used a constructivist grounded theory approach to interview 13 physician assessors about their perceptions of the effects of training and tool development on their conduct of assessments. RESULTS: Participants reported that training led them to realize that there is a potential for variability in assessors' judgments, prompting them to change their scoring and feedback behaviors to enhance consistency. However, many participants noted they had not substantially changed their numerical scoring. Nonetheless, most thought training would lead to increased standardization and consistency among assessors, highlighting a "standardization paradox" in which participants perceived a programmatic shift toward standardization but minimal changes in their own ratings. An "engagement effect" was also found in which participants involved in both tool development and training cited more substantial learnings than participants involved only in training. CONCLUSIONS: Findings suggest that training may help assessors recognize their own subjectivity when judging performance, which may prompt behaviors that support rigorous and consistent scoring but may not lead to perceptible changes in assessors' numeric ratings. Results also suggest that participating in tool development may help assessors align their judgments with the scoring criteria. Overall, results support the continued study of assessor training programs as a means of enhancing assessor consistency.
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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 | 0.113 | 0.250 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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 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".