Podcasts as Open‐Access Knowledge Dissemination Tools for Researchers: Lessons from Three Years of AMiNDR Podcast (A Month in Neurodegenerative Disease Research)
Bibliographic record
Abstract
Abstract Background Researchers are expected to manage various responsibilities whilst staying up‐to date with developments in their field. This is an arduous and time‐consuming task. In Alzheimer’s disease research alone, an average of 350 research articles are appearing every week on PubMed. We present the podcast AMiNDR (A Month in Neurodegenerative Disease Research), as a means of easing the burden on scientists. We offer an accessible channel where one can find all the primary papers sorted by topic in distinct episodes and summarised in an audio friendly format, complete with a bibliography of the papers covered as well as those not featured on the podcast. Methods We assess the success of this initiative on three levels: 1) We track our listenership and reach through podcast analytics on Simplecast including unique listeners, countries reached, and number of downloads per episode. 2) We evaluate the utility of our podcast based on listener retention, growth through time, and feedback collected through surveys and engagement indicators on social media. 3) We also consider the sustainability of our project through episode output, team growth, and internal interviews with volunteers to assess satisfaction. Results Since the launch of AMiNDR in June 2020, we have published over 350 episodes with accompanying bibliographies in 40 categories pertaining to Alzheimer’s disease research. Our podcast benefited from the contribution of 51 scientists worldwide at various stages of their training and reached over 6500 unique listeners in 91 countries. We present the results of the ongoing assessment of this initiative and measures we took to continue to refine our tool over the past three years, with a list of recommendations for digital knowledge dissemination tools like the AMiNDR podcast. Conclusion Based on our program evaluation, we find that AMiNDR is garnering a growing listenership worldwide and proving to be useful and accessible to researchers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".