Accuracy of self‐monitoring during learning of radiograph interpretation
Bibliographic record
Abstract
CONTEXT: Despite calls for the improvement of self-assessment as a basis for self-directed learning, instructional designs that include reflection in practice are uncommon. Using data from a screen-based simulation for learning radiograph interpretation, we present validity evidence for a simple self-monitoring measure and examine how it can complement skill assessment. METHODS: Medical students learning ankle radiograph interpretation were given an online learning set of 50 cases which they were asked to classify as 'abnormal' (fractured) or 'normal' and to indicate the degree to which they felt certain about their response (Definitely or Probably). They received immediate feedback on each case. All students subsequently completed two 20-case post-tests: an immediate post-test (IPT), and a delayed post-test (DPT) administered 2 weeks later. We determined the degree to which certainty (Definitely versus Probably) correlated with accuracy of interpretation and how this relationship changed between the tests. RESULTS: Of 988 students approached, 115 completed both tests. Mean ± SD accuracy scores decreased from 59 ± 17% at the IPT to 53 ± 16% at the DPT (95% confidence interval [CI] for the difference: -2% to -10%). Mean self-assessed certainty did not decrease (rates of Definitely: IPT, 17.6%; DPT, 19.5%; 95% CI for difference: +7.2% to -3.4%). Regression modelling showed that accuracy was positively associated with choosing Definitely over Probably (odds ratio [OR] 1.63, 95% CI 1.27-2.09) and indicated a statistically significant interaction between test timing and certainty (OR 0.72, 95% CI 0.52-0.99); thus, the accuracy of self-monitoring decayed over the retention interval, leaving students relatively overconfident in their abilities. CONCLUSIONS: This study shows that, in medical students learning radiograph interpretation, the development of self-monitoring skills can be measured and should not be assumed to necessarily vary in the same way as the underlying clinical skill.
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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 | 0.006 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".