Linking Podcasts With Social Media to Promote Community Health and Medical Research: Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Linking podcasts with social media is a strategy to promote and disseminate health and health research information to the community without constraints of time, weather, and geography. OBJECTIVE: To describe the process of creating a podcast library and promoting it on social media as a strategy for disseminating health and biomedical research topics to the community. METHODS: We used a community and patient engagement in research approach for developing a process to use podcasts for dissemination of health and health research information. We have reported the aspects of audience reach, impressions, and engagement on social media through the number of downloads, shares, and reactions posted on SoundCloud, Twitter, and Facebook, among others. RESULTS: In collaboration with our local community partner, we produced 45 podcasts focused on topics selected from a community health needs assessment with input from health researchers. Episodes lasted about 22 minutes and presented health-related projects, community events, and community resources, with most featured guests from Olmsted County (24/45, 53%). Health research was the most frequently discussed topic. Between February 2016 and June 2017, episodes were played 1843 times on SoundCloud and reached 1702 users on our Facebook page. CONCLUSIONS: This study demonstrated the process and feasibility of creating a content library of podcasts for disseminating health- and research-related information. Further examination is needed to determine the best methods to develop a sustainable social media plan that will further enhance dissemination (audience reach), knowledge acquisition, and communication of health topics.
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 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 | 0.058 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".