MP28: A randomized comparative trial of the usage, knowledge retention and media preferences in undergraduate medical students using podcasts and blog posts
Bibliographic record
Abstract
Introduction: Podcasts and blog posts are gaining popularity in Free Open Access Medical education (FOAMed). However, there remains a paucity of research comparing the two media for undergraduate medical education. This study aims to investigate if there are differences in medical students’ usage conditions, knowledge retention and preferences in the two types of media (podcasts, blog posts). Methods: Medical students were block-randomized to either the podcast or blog post group according to their year of schooling. They completed an online assessment of their baseline knowledge on the subject matter and preferences within the various types of media. Participants then received access to learning materials and were given four weeks to complete the follow-up assessment. Simple descriptive statistical data were used to detail student preferences. Paired samples t-tests and a Repeated Measures Analysis of Variance (RM-ANOVA) were conducted to assess knowledge acquisition. A carry forward analysis was used to impute missing data from students lost to follow-up. Results: A total of 65 medical students participated in our study (podcasts n=33, blog posts n=32). The initial survey suggests that students prefer general topic discussion and “approach-to” themes (68% and 84%, respectively). 55% of students in the podcast group preferred podcasts that were less than 30 minutes. None of the blog post group preferred a shorter text, and each blog post required a mean of 25 minutes to read. Completion of at least one follow-up assessment was comparable (68% podcasts, 70% blog posts). The podcast listeners tended to engage in multiple activities while using the learning material (e.g. at least 2-3 of the following: driving, eating, chores, taking notes, exercising), while the blog readers tended to do fewer activities (e.g. only 1 of the following: taking notes, eating, only reading). Both groups showed significant improvements in their test scores (Asthma: 22% improvement, Toxicology: 29%; p<0.01 for both), with blog posts demonstrating a larger but non-significant difference (RM-ANOVA, Topic*Modality F(1,59)=0.001, p=0.973). Conclusion: This study suggests that podcasts and blog posts significantly improve medical student knowledge retention to a similar degree, but differ in usage conditions.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".