Interactive Webcasting + Audioconferencing + Media Archiving for Medical eLearning
Bibliographic record
Abstract
Background: The standard approach to enhancing medical multimedia communications at a distance is videoconferencing [1]. Although videoconferencing ideally provides high-quality transmission of moving images and voices symmetrically among various sites, in practice it is limited to a small number of sites, is critically dependent upon having good bandwidth, and rarely incorporates effective mechanisms for archiving sessions in ways that allow flexible access to the content. Objective: We shall present an alternative approach, the use of highly interactive webcasting with integrated conferencing and the automatic Web publishing of structured, navigable, and searchable archives. We shall articulate the pedagogical and technical issues involved in enabling effective remote participation in events transmitted in this manner. Methods: In our poster and demonstration we shall exhibit key features of the design of our technology, which is called ePresence Interactive Media [2-5]. We shall illustrate use of the system by discussing how Computer Science graduate students and faculty from five Canadian universities used ePresence to participate in a Computer Supported Collaborative Work course offered at the University of Toronto during the fall semester 2005. We shall also summarize the experiences of a number of medical schools, hospitals, and medical research groups (University of Toronto, Memorial University of Newfoundland,
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".