Exploring the impact, challenges, and integration of podcasts in patient education: a systematic review
Bibliographic record
Abstract
BACKGROUND: Podcasts have become increasingly prominent as tools for health communication, offering flexible and accessible formats for patient education. While widely used in professional training, their role in supporting patient-centered learning remains underexplored. METHODS: This systematic review synthesized studies published from 2010 to 2024 concerning podcast use in patient education. Five databases-PubMed, Scopus, Web of Science, Google Scholar, and Embase-were searched using defined keywords. Studies were selected based on relevance to patient education through podcasts, and outcomes such as knowledge retention, comprehension, and engagement. Data extraction was performed independently by two reviewers. Quality assessment was conducted using the Cochrane Risk of Bias Tool, the Newcastle-Ottawa Scale, and the CASP checklist. A thematic synthesis approach was employed to analyze qualitative and quantitative findings. RESULTS: Of the twenty-one included studies, seven demonstrated improved patient knowledge retention, comprehension, and engagement through podcast use. Five studies emphasized accessibility and learner autonomy, highlighting the benefits of asynchronous and flexible delivery. Three studies explored the integration of podcasts with traditional teaching methods, showing positive outcomes when used as complementary tools. However, three studies identified challenges including content quality variability, limited production standards, and digital access disparities. Thematic synthesis categorized findings into four domains: educational effectiveness, integration with traditional education, accessibility and learner autonomy, and implementation challenges. CONCLUSIONS: Podcasts represent a promising adjunct to patient education. Their effectiveness depends on thoughtful design, contextual relevance, and equitable delivery. Standardization, blended learning models, and ongoing evaluation are essential for maximizing their impact. CLINICAL TRIAL NUMBER: Not applicable.
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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 | 0.004 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".