Embedding Medical Student Computer Tutorials into a Busy Emergency Department
Bibliographic record
Abstract
OBJECTIVES: To explore medical students' use of computer tutorials embedded in a busy clinical setting; to demonstrate that such tutorials can increase knowledge gain over and above that attributable to the clinical rotation itself. METHODS: Six tutorials were installed on a computer placed in a central area in an emergency department. Each tutorial was made up of between 33 and 85 screens of information that include text, graphics, animations, and questions. They were designed to be brief (10 minutes), focused, interactive, and immediately relevant. The authors evaluated the intervention using quantitative research methods, including usage tracking, surveys of faculty and students, and a randomized pretest-posttest study. RESULTS: Over 46 weeks, 95 medical students used the tutorials 544 times, for an overall average of 1.7 times a day. The median time spent on completed tutorials was 11 minutes (average [SD], 14 [+/-12] minutes). Seventy-four students completed the randomized study. They completed 65% of the assigned tutorials, resulting in improved examination scores compared with the control (effect size, 0.39; 95% confidence interval = 0.15 to 0.62). Students were positively disposed to the tutorials, ranking them as "valuable." Fifty-four percent preferred the tutorials to small group teaching sessions with a preceptor. The faculty was also positive about the tutorials, although they did not appear to integrate the tutorials directly into their teaching. CONCLUSIONS: Medical students on rotation in a busy clinical setting can and will use appropriately presented computer tutorials. The tutorials are effective in raising examination scores.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".