Global impact of a video education platform on oncology education amongst healthcare professionals.
Bibliographic record
Abstract
e23005 Background: Lower socioeconomic (SE) regions are often plagued extensively by healthcare outcome disparities. In searching for the root of this injustice, lack of access to the latest medical information has frequently been cited as a major contributor to these inequities. To address this, MedNews Week (MNW), a free, virtual education platform was developed to provide global health education through weekly oncology programming. The leading show, Keynote Conference, features live virtual presentations from oncology’s premier global leaders discussing the latest developments in the field. The aim of this study was to assess the global reach and impact of this platform for healthcare professionals (HCP). Methods: From September 2022 to January 2023, 37 MNW lectures on solid tumors and heme malignancies were showcased on VuMedi—a video platform providing free education content to HCP. Data collected from VuMedi included location, total page/video views, impressions, tumor subtype views and impressions, and occupation demographics. This information was analyzed to evaluate and assess the global reach of MNW on the VuMedi platform. Results: During this 5-month period, MNW experienced steady linear growth generating 5,643,257 page/video views with global viewership from 22 countries, including US, England, Canada, Brazil, and India (Table). Significant viewership was also observed from lower SE countries including Nigeria, Sudan, Georgia, and Bangladesh. Viewers were largely surgeons compared to Allied HCP (11:2). Higher viewership was observed for solid tumor presentations [706 (0.012%) page views and 9717 (7%) impressions] compared to heme malignancies [689 (0.001%) page views and 1688 (1.22%) impressions]. Within solid tumors, GI oncology keynotes were most viewed— generating 3755 (0.06%) page views and 28067 (20%) impressions, and the highest number of registrations recorded for this category. Conclusions: MNW continued growth in viewership and global reach amongst HCPs and demonstrates its emergence as a viable outlet to contribute to global oncology education, especially in lower SE areas. While previous studies have identified internet access and cost as barriers to high-quality medical information, MNW has experienced steady growth and reachability especially within these limited areas. The platform’s ability to showcase global leaders to HCP offers a practical approach to combat educational inequity and positively impact oncology education globally. [Table: see text]
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 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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 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".