Adding Live-Streaming to Recorded Lectures in a Non-Distributed Pre-Clerkship Medical Education Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Live-streaming video has had increasing uses in medical education, especially in distributed education models. The literature on the impact of live-streaming in non-distributed education models, however, is scarce. OBJECTIVES: To determine the attitudes towards live-streaming and recorded lectures as a resource to pre-clerkship medical students in a non-distributed medical education model. METHODS: First and second year medical students were sent a voluntary cross-sectional survey by email, and were asked questions on live-streaming, recorded lectures and in person lectures using a 5-point Likert and open answers. RESULTS: Of the 118 responses (54% response rate), the data suggested that both watching recorded lectures (Likert 4.55) and live-streaming lectures (4.09) were perceived to be more educationally valuable than face-to-face attendance of lectures (3.60). While responses indicated a statistically significant increase in anticipated classroom attendance if both live-streaming and recorded lectures were removed (from 63% attendance to 76%, p =0.002), there was no significant difference in attendance if live-streaming lectures were removed but recorded lectures were maintained (from 63% to 66%, p=0.76). CONCLUSION: The addition of live-streaming lectures in the pre-clerkship setting was perceived to be value added to the students. The data also suggests that the removal of live-streaming lectures would not lead to a statistically significant increase in classroom attendance by pre-clerkship students.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.022 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it