A Randomized Comparative Trial of the Knowledge Retention and Usage Conditions in Undergraduate Medical Students Using Podcasts and Blog Posts
Bibliographic record
Abstract
Introduction Podcasts and blog posts have gained popularity in Free Open Access Medical education (FOAM). Previous work suggests that podcasts may be useful for knowledge acquisition in undergraduate medical education. However, there remains a paucity of research comparing the two mediums. This study aims to investigate if there are differences in knowledge acquisition and usage conditions by medical students using podcasts and blog posts. Methods Medical students were randomized to either the podcast or blog post group. They completed an initial online assessment of their baseline knowledge on the subject matter. Participants then received access to learning materials and were given four weeks to complete the follow-up assessment on their own time. Independent t-test, paired samples t-test, and a mixed ANOVA (analysis of variance) were conducted to assess knowledge acquisition. An intention-to-teach analysis was used to impute missing data from students lost to follow-up. Simple descriptive statistical data was used to describe media usage conditions. Results Completion of at least one follow-up assessment was comparable (68% podcasts (n = 21/31), 73% blog posts (n = 22/30)). Both groups showed significant improvements in their test scores, with an average 22% improvement for the podcast group and 29% for the blog post group. There was no significant statistical difference in knowledge acquisition between educational modalities overall. Students in the blog post group that completed both post-intervention quizzes showed a larger improvement than the podcast group in the toxicology topic, with similar improvements in the asthma topic. The podcast group tended to engage in multiple activities while using the learning materials (e.g. at least two to three of the following: driving, eating, chores, taking notes, exercising/walking), while the blog readers tended to do fewer activities (e.g. only one of the following: note taking, eating). Conclusion This study suggests that podcasts and blog posts are useful for extracurricular knowledge acquisition by undergraduate medical students with no significant difference between the two modalities. The usage conditions for each type of media differ.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".