A validity evaluation of lexicon‐based sentiment analysis of medical students' clinical performance from in‐training evaluation reports
Bibliographic record
Abstract
INTRODUCTION: Assessment of clinical performance has traditionally been a numbers game based upon Likert scale ratings. But, thanks to advances in the science of natural language processing (NLP), it is now possible to incorporate rich narrative data into assessment. In this study, our objective was to evaluate the validity of lexicon-based sentiment analysis of medical students' clinical performance from in-training evaluation reports (ITERs) with a view to fully integrating this as a machine-based process into future assessment decisions. METHODS: This was a mixed methods, retrospective derivation/validation cohort study structured around Kane's validity framework. We used content analysis to create a lexicon of performance descriptors, performed a G-study, and calculated the positive likelihood ratio (LR+) for descriptors (scoring). To evaluate generalisation, we calculated the intraclass correlation coefficient and compared descriptors in derivation and validation cohorts. We then performed [human] lexicon-based sentiment analysis and compared the number of descriptors of different types between cohorts of highest performing students (HPS) and lowest performing students (LPS). RESULTS: In our G-study, 86.6% of variance was attributed to the student. The ICC between raters for identification of descriptors was 0.93. The mean number of neutral descriptors was similar between HPS and LPS cohorts, but the number of negative descriptors was higher for LPS (11.4 (10.8) versus 1.4 (1.6) for HPS, p < 0.01, d = 1.37) and the number of positive descriptors was higher for HPS (19 (14) versus 1.4 (1.5) for LPS, p < 0.001, d = 1.86). DISCUSSION: In the midst of their busy clinical work schedule, preceptors find time to tell a story about a medical student and these narrative data enrich the assessment portfolio. Based upon our validity argument, we feel there is a role for lexicon-based sentiment analysis of clinical performance descriptors in ITERs and that these results can contribute meaningfully to assessment decisions.
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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 | 0.066 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".