Evaluation of videoconferenced grand rounds
Bibliographic record
Abstract
We evaluated various aspects of grand rounds videoconferenced from a tertiary care hospital to a regional hospital in Nova Scotia. During a five-month study period, 29 rounds were broadcast (19 in medicine and 10 in cardiology). The total recorded attendance at the remote site was 103, comprising 70 specialists, nine family physicians and 24 other health-care professionals. We received 55 evaluations, a response rate of 53%. On a five-point Likert scale (on which higher scores indicated better quality), mean ratings by remote-site participants of the technical quality of the videoconference were 3.0-3.5, with the lowest ratings being for ability to hear the discussion (3.0) and to see visual aids (3.1). Mean ratings for content, presentation, discussion and educational value were 3.8 or higher. Of the 49 physicians who presented the rounds, we received evaluations from 41, a response rate of 84%. The presenters rated all aspects of the videoconference and interaction with remote sites at 3.8 or lower. The lowest ratings were for ability to see the remote sites (3.0) and the usefulness of the discussion (3.4). We received 278 evaluations from participants at the presenting site, an estimated response rate of about 55%. The results indicated no adverse opinions of the effect of videoconferencing (mean scores 3.1-3.3). The estimated costs of videoconferencing one grand round to one site and four sites were C dollars 723 and C dollars 1515, respectively. The study confirmed that videoconferenced rounds can provide satisfactory continuing medical education to community specialists, which is an especially important consideration as maintenance of certification becomes mandatory.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".