How supervision and educational supports impact medical students’ preparation for future learning of endotracheal intubation skills: a non-inferiority experimental trial
Bibliographic record
Abstract
Abstract Background Professional education cannot keep pace with the rapid advancements of knowledge in today’s society. But it can develop professionals who can. ‘Preparation for future learning’ (PFL) has been conceptualized as a form of transfer whereby learners use their previous knowledge to learn about and adaptively solve new problems. Improved PFL outcomes have been linked to instructional approaches targeting learning mechanisms similar to those associated with successful self-regulated learning (SRL). We expected training that includes evidence-based SRL-supports would be non-inferior to training with direct supervision using the outcomes of a ‘near transfer’ test, and a PFL assessment of simulated endotracheal intubation skills. Method This study took place at the University of Toronto from October 2014 to August 2015. We randomized medical students and residents (n = 54) into three groups: Unsupervised, Supported; Supervised, Supported; and Unsupervised, Unsupported. Two raters scored participants’ test performances using a Global Rating Scale with strong validity evidence. We analyzed participants’ near transfer and PFL outcomes using two separate mixed effects ANCOVAs. Results For the Unsupervised, Supported group versus the Supervised, Supported group, we found that the difference in mean scores was 0.20, with a 95% Confidence Interval (CI) of − 0.17 to 0.57, on the near transfer test, and was 0.09, with a 95% CI of − 0.28 to 0.46, on the PFL assessment. Neither mean score nor their 95% CIs exceeded the non-inferiority margin of 0.60 units. Compared to the two Supported groups, the Unsupervised, Unsupported group was non-inferior on the near transfer test (differences in mean scores were 0.02 and − 0.22). On the PFL assessment, however, the differences in mean scores were 0.38 and 0.29, and both 95% CIs crossed the non-inferiority margin. Conclusions Training with SRL-supports was non-inferior to training with a supervisor. Both interventions appeared to impact PFL assessment outcomes positively, yet inconclusively when compared to the Unsupervised and Unsupported group, By contrast, the Unsupervised, Supported group did not score well on the near transfer test. Based on the observed sensitivity of the PFL assessment, we recommend researchers continue to study how such assessments may measure learners’ SRL outcomes during structured learning experiences.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".