Accuracy of self‐monitoring: does experience, ability or case difficulty matter?
Bibliographic record
Abstract
CONTEXT: The ability to self-monitor one's performance in clinical settings is a critical determinant of safe and effective practice. Various studies have shown this form of self-regulation to be more trustworthy than aggregate judgements (i.e. self-assessments) of one's capacity in a given domain. However, little is known regarding what cues inform learners' self-monitoring, which limits an informed exploration of interventions that might facilitate improvements in self-monitoring capacity. The purpose of this study is to understand the influence of characteristics of the individual (e.g. ability) and characteristics of the problem (e.g. case difficulty) on the accuracy of self-monitoring by medical students. METHODS: In a cross-sectional study, 283 medical students from 5 years of study completed a computer-based clinical reasoning exercise. Confidence ratings were collected after completing each of six cases and the accuracy of self-monitoring was considered to be a function of confidence when the eventual answer was correct relative to when the eventual answer was incorrect. The magnitude of that difference was then explored as a function of year of seniority, gender, case difficulty and overall aptitude. RESULTS: Students demonstrated accurate self-monitoring by virtue of giving higher confidence ratings (57.3%) and taking a shorter time to work through cases (25.6 seconds) when their answers were correct relative to when they were wrong (41.8% and 52.0 seconds, respectively; p< 0.001 and d > 0.5 in both instances). Self-monitoring indices were related to student seniority and case difficulty, but not to overall ability or student gender. CONCLUSIONS: This study suggests that the accuracy of self-monitoring is context specific, being heavily influenced by the struggles students experience with a particular case rather than reflecting a generic ability to know when one is right or wrong. That said, the apparent capacity to self-monitor increases developmentally because increasing experience provides a greater likelihood of success with presented problems.
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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 | 0.005 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".