E-learning for chest x-ray interpretation improves medical student skills and confidence levels
Bibliographic record
Abstract
BACKGROUND: Radiology is an important aspect of medicine to which medical students often do not receive sufficient exposure. The aim of this project was to determine whether the integration of an innovative e-learning module on chest x-ray interpretation of the heart would enhance the radiological interpretive skills, and improve the confidence, of first year graduate entry medical students. METHODS: All first-year graduate entry (all students had a prior university degree) medical students at the University of Limerick (n = 152) during academic year 2015-16 were invited to participate in this study. An assessment instrument was developed which consisted of 5 radiological cases to be interpreted over a designated and supervised 15-min time period. Students underwent a pre-, mid- and post-intervention assessment of their radiology interpretative skills. An online e-module was provided following the pre-test and additional practice cases were provided following the mid-intervention test. Assessment scores and confidence levels were compared pre-, mid- and post-intervention. RESULTS: The overall performance (out of a total score of 25) for the 87 students who completed all three assessments increased from 13.2 (SD 3.36) pre-intervention to 14.3 (SD 2.97) mid-intervention to 15.8 (SD 3.40) post-intervention. This change over time was statistically significant (p < 0.001) with a medium effect size (eta-squared = 0.35). Increases from pre- to post-intervention were observed in each of the five areas assessed, although performance remained poor in diagnosis post-intervention. Of the 118 students who provided feedback after the intervention, 102 (86.4%) stated that they would recommend the resource to a colleague to improve their interpretative skills. CONCLUSIONS: This study suggests that early exposure to e-learning radiology modules is beneficial in undergraduate medical school curricula. Further studies are encouraged to assess how long the improvement may last before attrition.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".