Using a Computer Module to Teach Use of the EpiPen®
Bibliographic record
Abstract
Background: The medical literature suggests that patients and physicians are deficient in their ability to use a self-injectable epinephrine device (EpiPen®) for management of anaphylaxis. This study aims to determine whether a computer module is an effective tool for the instruction of a technical skill to medical trainees.Methods: We conducted a two group comparison study of 35 Post-Graduate Year 1 and 2 Family Medicine residents. Participants were instructed on use of the EpiPen® using either a written module or a computer module. Participants were evaluated on use of the EpiPen® using standardized objective outcome measures by a blinded assessor. Assessments took place prior to and following instruction, using the assigned learning modality.Results: There were 34 participants who completed the study. Both groups demonstrated significant improvement in demonstrating use of the EpiPen® following training (p <0.001 for both). A significant post-training difference favouring the computer module learners over the written module learners was observed (p = 0.035). However, only 53% and 18% of candidates (computer module and written module, respectively) were able to correctly perform all of the checklist steps.Conclusion: While our findings suggest computer modules represent an effective modality for teaching use of the EpiPen® to medical trainees, the low number of candidates who were able to perform all the checklist items regardless of modality needs to be addressed.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".