Reinforcing outpatient medical student learning using brief computer tutorials: the Patient-Teacher-Tutorial sequence
Bibliographic record
Abstract
BACKGROUND: At present, what students read after an outpatient encounter is largely left up to them. Our objective was to evaluate the education efficacy of a clinical education model in which the student moves through a sequence that includes immediately reinforcing their learning using a specifically designed computer tutorial. METHODS: Prior to a 14-day Pediatric Emergency rotation, medical students completed pre-tests for two common pediatric topics: Oral Rehydration Solutions (ORS) and Fever Without Source (FWS). After encountering a patient with either FWS or a patient needing ORS, the student logged into a computer that randomly assigned them to either a) completing a relevant computer tutorial (e.g. FWS patient + FWS tutorial = "in sequence") or b) completing the non-relevant tutorial (e.g. FWS patient + ORS tutorial = "out of sequence"). At the end of their rotation, they were tested again on both topics. Our main outcome was post-test scores on a given tutorial topic, contrasted by whether done in- or out-of-sequence. RESULTS: Ninety-two students completed the study protocol with 41 in the 'in sequence' group. Pre-test scores did not differ significantly. Overall, doing a computer tutorial in sequence resulted in significantly greater post-test scores (z-score 1.1 (SD 0.70) in sequence vs. 0.52 (1.1) out-of-sequence; 95% CI for difference +0.16, +0.93). Students spent longer on the tutorials when they were done in sequence (12.1 min (SD 7.3) vs. 10.5 (6.5)) though the difference was not statistically significant (95% CI diff: -1.2 min, +4.5). CONCLUSIONS: Outpatient learning frameworks could be structured to take best advantage of the heightened learning potential created by patient encounters. We propose the Patient-Teacher-Tutorial sequence as a framework for organizing learning in outpatient clinical settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".