Are two heads better than one? Comparing dyad and self‐regulated learning in simulation training
Bibliographic record
Abstract
CONTEXT: The optimal learner to simulator ratio for procedural skills training is not known. Research in motor learning suggests observational training in pairs, termed 'dyad training', may be as effective as directed self-regulated learning (DSRL). OBJECTIVES: This study was conducted to compare the relative effectiveness and efficiency of dyad versus DSRL training of simulation-based lumbar puncture (LP). METHODS: We conducted a two-group randomised equivalence trial. First-year internal medicine residents (n = 50) were randomly assigned to learn LP either in dyads or as individual learners on a simulator, using a directed self-regulated approach (i.e. the learning sequence was defined for them, but they defined the pace of learning). Participants were videotaped performing a simulated LP on a pre-test, an immediate post-test, and a 6-week delayed retention test. In duplicate, blinded raters independently evaluated all trainee performances using a previously validated 5-point global rating scale (GRS) and 35-item checklist. RESULTS: Our analyses showed no significant differences (p = 0.69) on pre-test, post-test or retention test GRS scores between the dyad (mean ± standard deviation [SD] scores by test: 2.39 ± 0.57, 3.48 ± 0.62, 3.12 ± 0.85, respectively) and DSRL (mean ± SD scores by test: 2.67 ± 0.50, 3.34 ± 0.77, 3.21 ± 0.79, respectively) groups. Both groups improved significantly from pre-test to post-test (p < 0.001) and retained that performance following the 6-week delay. Dyad participants experienced significantly greater pre-test to post-test gains than DSRL participants (p = 0.02). There was no significant difference in total practice time between the groups (20.94 minutes for individuals and 24.20 minutes for dyads; p = 0.175). CONCLUSIONS: Our results indicate that learning in pairs is as effective as independent DSRL. Dyad training permits the more efficient use of simulators as two learners use the same resources as an individual.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".