P‐ED‐6 | Transfusion Medicine: Continuing Education via Digital Programming
Bibliographic record
Abstract
Continuing education is critical for healthcare professionals, including those working in transfusion medicine (TM), to stay updated on the latest advancements, techniques, and research in their field, maintain quality patient care, and, in some cases, maintain professional certification. Digital models have effectively delivered high-quality continuing education during the COVID-19 pandemic and beyond. The current program aims to provide educational programs in TM via digital platforms and to maximize engagement. Educational materials were provided via podcasts and webinars. Monthly podcasts delivered a broad range of topics on science and technology pertinent to lab professionals in an engaging, interactive format. Webinars with defined learning objectives and traditional speaker presentations provided in-depth knowledge about lab procedures and emerging concepts in a more structured format. The webinars were presented to live attendees and were later available for on-demand viewing; some webinars were accredited through professional agencies to allow the option of receiving professional continuing education credits. In addition, a periodic gathering of metrics measuring interaction and engagement was performed. Podcast engagement was measured by total downloads, and reach was assessed by analysis of downloads by country. A total of 11 podcasts were released from January through December 2022. As of December 2022, the podcasts were downloaded over 6000 times, with downloads most frequently occurring in the United States, Canada, and the United Kingdom. To date, the most commonly downloaded podcast discusses the resolution of antibodies to high-prevalence antigens. Webinars, most frequently attended by lab technicians and those in lab management, garnered 3580 views in 2022. A total of 10 webinars, 5 live and 5 simu-live webinars, were offered in 2022 and attended by 1431 live attendees and 2149 on-demand attendees. Live events averaged 286 live attendees, a 63% increase over the 2015-2018 event attendee average and a 47% increase over 2021 attendance. Simu-live attendance has increased by 272% since 2021. Metrics include the number of attendees by country and breakdown by participant credentials. Digital platforms have proven effective media for the worldwide delivery of continuing education in TM. The flexibility of podcasts adds to their appeal and accessibility, while the more traditional webinar presentation provides the benefits of an in-person conference without traveling to a physical location. With digital continuing education, TM professionals have been afforded a convenient, cost- and time-effective way to stay updated on the latest developments in their field despite the challenges posed by the COVID-19 pandemic.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.350 | 0.121 |
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".