An Integrated Interactive-Spaced Education Radiology Curriculum for Preclinical Students
Bibliographic record
Abstract
Introduction: The objective of this study is to determine whether a radiology module, together with online spaced education, helps students of an integrated problem-based learning (PBL) curriculum increase their radiology knowledge and long-term retention. Materials and Methods: Second-year students at the American University of Antigua College of Medicine participated in small groups of ten students each into two 2 h of radiology laboratories. The study comprised two cohorts: winter and fall 2013 students (control group) and 2014 students (experimental group). Both groups used face-to-face PBL. The students of the experimental group received additional online-spaced education. The skills were assessed for both groups before the beginning of laboratories and 4 weeks and 7 months after laboratories. Results: There was no significant difference on pretest between the control and experimental groups. On completion of the radiology laboratories, comparison of test results before and after training showed net improvement for both groups. The corresponding difference for the experimental group was higher compared to the one for the control group (7.83 vs. 6.21,P< 0.001). The difference between the scores on delayed test and pretest showed that the students of both groups demonstrated average knowledge improvement even though their level of performance was slightly below the posttest. The corresponding difference for the experimental group did not differ much from the posttest (P> 0.05), and no significant difference of scores was observed 7 months later for either group. Further, a higher percentage of the students in the experimental group strongly agreed that their learning objectives were met (92% vs. 71%,P> 0.001), and this trend persisted throughout the study. Conclusion: Online spaced education combined to a face-to-face PBL enhances not only the student's knowledge of basic radiology along with his/her self-assessment skills but also the long-term retention of radiology material and satisfaction with the integrated interactive system-based module. Future research is needed to see if medical students in need of additional education support may benefit from spaced education in the field of remediation.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".