Redeveloping a workplace-based assessment program for physicians using Kane’s validity framework
Bibliographic record
Abstract
This paper describes the use of Kane’s validity framework to redevelop a workplace-based assessment program for practicing physicians administered by the College of Physicians and Surgeons of Ontario. The developmental process is presented according to the four inferences in Kane’s model. Scoring was addressed through the creation of specialty-specific assessment criteria and global, narrative-focused reports. Generalization was addressed through standardized sampling protocols and assessor training and consensus-building. Extrapolation was addressed through the use of real-world performance data and an external review of the scoring tools by practicing physicians. Implications were theoretically supported through adherence to formative assessment principles and will be assessed through an evaluation accompanying the implementation of the redeveloped program. Kane’s framework was valuable for guiding the redevelopment process and for systematically collecting validity evidence throughout to support the use of the assessment for its intended purpose. As the use of workplace-based assessment programs for physicians continues to increase, practical examples are needed of how to develop and evaluate these programs using established frameworks. The dissemination of comprehensive validity arguments is vital for sharing knowledge about the development and evaluation of WBA programs and for understanding the effects of these assessments on physician practice improvement.
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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 | 0.286 | 0.306 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".