The Power of Subjectivity in the Assessment of Medical Trainees
Bibliographic record
Abstract
Objectivity in the assessment of students and trainees has been a hallmark of quality since the introduction of multiple-choice items in the 1960s. In medical education, this has extended to the structured examination of clinical skills and workplace-based assessment. Competency-based medical education, a pervasive movement that started roughly around the turn of the century, similarly calls for rigorous, objective assessment to ensure that all medical trainees meet standards to assure quality of health care. At the same time, measures of objectivity, such as reliability, have consistently shown disappointing results. This raises questions about the extent to which objectivity in such assessments can be ensured.In fact, the legitimacy of "objective" assessment of individual trainees, particularly in the clinical workplace, may be questioned. Workplaces are highly dynamic and ratings by observers are inherently subjective, as they are based on expert judgment, and experts do not always agree-for good, idiosyncratic, reasons. Thus, efforts to "objectify" these assessments may be problematically distorting the assessment process itself. In addition, "competence" must meet standards, but it is also context dependent.Educators are now arriving at the insight that subjective expert judgments by medical professionals are not only unavoidable but actually should be embraced as the core of assessment of medical trainees. This paper elaborates on the case for subjectivity in assessment.
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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 | 0.310 | 0.457 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.004 | 0.061 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".