Creation of the ECHO Idaho Podcast: Tutorial and Pilot Assessment
Bibliographic record
Abstract
Background: Project ECHO (Extension for Community Health Outcomes) is an innovative program that uses videoconferencing technology to connect health care providers with experts. The model has been successful in reaching health care providers in rural and underserved areas and positively impacting clinical practice. ECHO Idaho, a replication partner, has developed programming that has increased knowledge and confidence of health care professionals throughout the state of Idaho, United States. Although the ECHO model has a demonstrated ability to recruit, educate, and train health care providers, barriers to attending Project ECHO continuing education (CE) programs remain. The asynchronous nature of podcasts could be used as an innovative medium to help address barriers to CE access that health care professionals face. The ECHO Idaho "Something for the Pain" podcast was developed to increase CE accessibility to rural and frontier providers, while upscaling their knowledge of and competence to treat and assess substance use disorders, pain, and behavioral health conditions. Objective: This paper describes the creation and preliminary assessment of the ECHO Idaho "Something for the Pain" podcast. Methods: Podcast episodes consisted of interviews with individuals as well as didactic lectures. Audio from these recordings were edited for content and length and then professionally reviewed by subject matter experts (eg, featured episode speakers). Target audiences consisted of health care providers and community members interested in behavioral health and substance use disorders. Metrics on podcast listeners were assessed using SoundCloud's RSS feed, continuing education survey completion, and iECHO. Results: The ECHO Idaho "Something for the Pain" podcast's inaugural season comprised 14 episodes with 626 minutes of CE material. The podcast series received a total of 2441 listens from individuals in 14 different cities across Idaho, and 63 health care providers listened and claimed CE credits. The largest professional group was social workers (n=22; 35%). Conclusions: We provide preliminary evidence that podcasts can be used to provide health care providers with opportunities to access CE material. Health care providers listened to and claimed CE credits from the ECHO Idaho "Something for the Pain" podcast. Project ECHO programs should consider creating podcasts as an additional platform for disseminating ECHO material.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".