Use of podcast technology to facilitate education, communication and dissemination in palliative care: the development of the AmiPal podcast
Bibliographic record
Abstract
OBJECTIVES: Podcasts have the potential to facilitate communication about palliative care with researchers, policymakers and the public. Some podcasts about palliative care are available; however, this is not reflected in the academic literature. Further study is needed to evaluate the utility of podcasts to facilitate knowledge-transfer about subjects related to palliative care. The aims of this paper are to (1) describe the development of a palliative care podcast according to international recommendations for podcast quality and (2) conduct an analysis of podcast listenership over a 14-month period. METHODS: The podcast was designed according to internationally agreed quality indicators for medical education podcasts. The podcast was published on SoundCloud and was promoted via social media. Data were analysed for frequency of plays and geographical location between January 2015 and February 2016. RESULTS: 20 podcasts were developed which were listened to 3036 times (an average of 217 monthly plays). The Rich Site Summary feed was the most popular way to access the podcast (n=1937; 64%). The mean duration of each podcast was 10 min (range 3-21 min). The podcast was listened to in 68 different countries and was most popular in English-speaking areas, of which the USA (n=1372, 45.2%), UK (n=661, 21.8%) and Canada (n=221, 7.3%) were most common. CONCLUSIONS: A palliative care podcast is a method to facilitate palliative care discussion with global audience. Podcasts offer the potential to develop educational content and promote research dissemination. Future work should focus on content development, quality metrics and impact analysis, as this form of digital communication is likely to increase and engage wider society.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".