The Mean Delta Method: Quantifying Assessor Stringency and Leniency and Identifying Outliers in Workplace-Based Assessments
Bibliographic record
Abstract
ABSTRACT: Assessor stringency and leniency (ASL)-an assessor's tendency to award low or high scores-has a significant effect on workplace-based assessments. Outliers on this spectrum have a disproportionate effect. However, no method has been published for quantifying ASL or identifying outlier stringent or lenient assessors using workplace-based assessment data. The authors propose the mean delta method, which compares the scores that an assessor awards to trainees with those trainees' mean scores. This novel, simple method can be used to quantify ASL and identify outlier assessors without requiring specialized statistical knowledge or software. As a worked example, the mean delta method was applied to a set of end-of-shift assessments completed in a large Canadian academic emergency department from July 1, 2017, to May 31, 2018, and used to examine the net effect of ASL on learners' assessment scores. A total of 3,908 assessments were completed by 99 assessors for 151 trainees, with a median (interquartile range) of 37 (12-39) completed assessments per trainee. Using cutoff values of 1.5 and 2 standard deviations, a total of 11 and 3 outlier assessors were identified, respectively. Moreover, ASL changed overall scores by more than the mean difference between years of training for nearly 1 in 4 learners. The mean delta method was able to quantify ASL and identify outlier lenient and stringent assessors. It was also used to quantify the net effect of ASL on individual trainees. This method could be used to further study outlier assessors, to identify assessors who may benefit most from targeted coaching and feedback, and to measure changes in assessors' tendencies over time or with specific intervention.
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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 | 0.038 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".