Documenting medical students’ use of self-explanations: tool development and initial validity evidence
Bibliographic record
Abstract
Introduction: Self-explanation (SE), an individual learning strategy for the development of clinical reasoning skills, has been implemented in undergraduate medical curricula. A tool for documenting students' appropriate use of SE is needed to ensure benefit on learning. The objective of this project was to develop such a tool and report on evidence of its validity. Methods: We used DeVellis's eight steps to develop the tool. Assessors applied the tool to 85 audio-recorded SEs. We calculated students' mean number of inferences (biomedical, clinical, monitoring) and case elements used when self-explaining to document validity evidence of content. We used interrater agreement, with intraclass correlation coefficients, to document validity evidence of response processes. Results: Three assessors documented the students' use of SE with the tool. They found means of 13.33 to 17.76 biomedical inferences, 16.27 to 27.04 clinical inferences, 2.03 to 3.10 monitoring statements, and listed 21.77 to 26.87 case elements. Interrater reliability varied from 0.53 to 0.96. Discussion: We developed the tool using the principles underlying SE. The way students used SE aligned with our expectations. Assessors used the tool in a consistent way. The tool could document students' use of SE in experimental or educational contexts.
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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 | 0.226 | 0.371 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 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".