Videoconferenced grand rounds: Needs assessment for community specialists
Bibliographic record
Abstract
INTRODUCTION: Grand rounds are a traditional means of continuing education for specialist physicians. The purpose of this study was to determine the need for and feasibility of interactive videoconferenced grand rounds between an academic health center and community specialists practicing in the three provinces served by the health center. METHODS: Using questionnaires, we studied two populations: the academic center's clinical department and division heads and community specialists in three provinces. RESULTS: We received 27 of 34 (79%) questionnaires from department heads. Nine reported that they already videoconferenced their rounds, 12 expressed a willingness to do so, and 4 responded that they may be interested. Fourteen departments responded that they were willing to include community specialists in planning and presenting. Using a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree), respondents rated the statement "Regional specialists would benefit from videoconferenced grand rounds" as 4.2. The return rate from community specialists was 333 of 876 (38%), of which 274 indicated that they would attend videoconferenced rounds, 42 said "maybe," and 9 said "no." Using the same 5-point scale, respondents rated both the following statements as 3.8: "Videoconferenced grand rounds would benefit me" and "These rounds would help me keep in touch with my colleagues." One hundred and two (31%) indicated that they would help plan rounds from the academic center. DISCUSSION: This study demonstrated the willingness on the part of one academic center to videoconference grand rounds to community specialists and interest from community specialists in participating. It raises logistical and educational issues, including scheduling and how to effectively include community physicians in needs assessment and planning. As requirements for specialists to participate in accredited learning activities become more rigorous, videoconferencing grand rounds may be one way to increase access to important learning activities.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".