MacEmerg Podcast: A Novel Initiative to Connect a Distributed Community of Practice
Bibliographic record
Abstract
BACKGROUND: Regional knowledge dissemination and information sharing is a challenge among physically divided groups of physicians. Many staff and resident physicians do not have easy access to share clinical and medical education and research information with each other in an academic setting. Our divisions of emergency medicine could benefit from a novel approach aimed at improving overall connection and collaborative engagement. INNOVATION: By harnessing the sociomateriality properties of podcasting, we could achieve the dual goals of better connecting our faculty as well as educating the audience on aspects of clinical practice and education that are especially relevant to our region. We sought to primarily draw on local expertise for content. We developed a standardized structure for our monthly releases, with each episode composed of a main faculty segment, a resident-focused segment, and a medical education segment. Accessibility to the podcast was maximized through its publication across multiple platforms and detailed individual show notes were made available. OUTCOMES: We applied logic model methodology with the intended goal of having much of our content consumed by local faculty and trainees. Using Web-based analytic data, we were able to ascertain the proportion and number of listens that occurred from within our local university-affiliated and/or catchment region. Episodes averaged 227.7 ± 67.2 listens with an overall 44.1% of those originating from within our defined region. REFLECTION: Given the number of regional listeners we are consistently reaching, we have been effective in serving to connect a widely distributed group of academic physicians. As we continue to grow the podcast, we plan on collecting quantitative data to better ascertain its effect on our stated goals.
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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 | 0.024 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".