The impact of driving versus undistracted listening on podcast knowledge acquisition and retention using a driving simulator: A randomized, cross-over trial
Bibliographic record
Abstract
INTRODUCTION: Research on listening to podcasts while driving suggested no significant difference compared to undistracted listening. However, these studies were conducted in non-controlled driving environments, limiting the evaluation of the environment's impact. This study aimed to compare knowledge acquisition and retention among resident physicians and undergraduate students while listening to medical education podcasts in a controlled, simulator-based, driving environment versus an undistracted listening condition. METHODS: A randomized, crossover trial involved 19 residents and 22 undergraduate students from McMaster university and McMaster hospital. Participants listened to podcasts while driving in an immersive, high-fidelity motion simulator that mimics different driving environments: a high-distraction city environment and a low-distraction country environment. In the undistracted listening condition, participants listened to podcasts while being seated at a desk. Immediate and delayed recall tests after a month were administered, and data were analyzed using a 2x2 mixed ANOVA. RESULTS: There were no significant differences in knowledge acquisition (e.g., accuracy) between the undistracted, city driving, and country driving conditions (p > 0.05, η2 = 0.011). However, the country driving condition demonstrated slightly higher accuracy compared to the city driving condition in the immediate assessment condition (p < 0.05, η2 = 0.018). Medical expertise level (i.e., resident vs student) did not affect knowledge acquisition across different listening conditions (p > 0.05, η2 = 0.007). CONCLUSION: In a simulated environment, knowledge acquisition from a podcast is not compromised by the attention needed for driving a vehicle. The two distraction levels used in this experiment showed no significant interference with knowledge acquisition. This holds true regardless of participants' medical expertise. This study highlights the potential of incorporating podcasts into daily commuting to support ongoing education without requiring dedicated study time, enhancing both flexibility and efficiency in professional development.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".