Evaluating Computer-Assisted Learning for Common Pediatric Emergency Procedures
Bibliographic record
Abstract
OBJECTIVE: To develop a computer-assisted learning (CAL) model and evaluate its effectiveness in improving medical trainees' knowledge of pediatric emergency procedures. METHODS: Three common pediatric emergency procedures were selected: laceration repair, splinting of fractures, and lumbar puncture. Web-based computerized tutorials were developed using digital images, short video clips, and instructional text. The tutorials focused on indications/contraindications of procedure, steps of the procedure, and materials needed. A 20-item multiple-choice examination was developed to test content covered in the tutorials. Twenty-three, third- and fourth-year medical students were randomly assigned to the CAL group or the nonintervention group. The 20-item multiple-choice examination was given to the trainee either before or after completion of the tutorials, based on group assignment. Both groups completed demographic information forms. RESULTS: The 13 students in the intervention group had a significantly higher average examination score, 16.3 (81.5%) (SD, 2.68), compared with the average score of the nonintervention group, 10.9 (54.5%) (SD, 1.37) (Wilcoxon test P = 0.00001). There was no significant difference between the 2 groups. CONCLUSION: The CAL model improves students' knowledge of emergency procedures. This model can be used as an adjuvant to traditional teaching of emergency procedures. Strategies for their optimal use need to be explored and evaluated.
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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 | 0.003 | 0.013 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".