A comparison of telemedicine teaching to in-person teaching for the acquisition of an ultrasound skill - a pilot project
Bibliographic record
Abstract
Telemedicine is widely used for medical education but few studies directly investigate how telemedicine teaching compares to conventional in-person teaching. Here we determine whether telemedicine teaching is as effective as in-person teaching for the acquisition of an ultrasound skill important in trauma care. Nurses with no prior ultrasound experience (n = 10) received study material and a teaching session on how to locate and image the hepatorenal space (Morison's pouch). One group of nurses was taught in-person (In-person Group) and the other group was taught via telemedicine (Telemedicine Group). Telemedicine allowed two-way audio and visual communication between the instructor and the nurses. A comparison of the teaching techniques showed that telemedicine teaching was equivalent to in-person teaching for the acquisition of practical and theoretical skills required to locate Morison's pouch. The average time required to locate Morison's pouch after teaching was similar between both groups. The results demonstrate that telemedicine teaching is as effective as in-person teaching for the acquisition of bedside ultrasound skills necessary to identify Morison's pouch. Remote teaching of these bedside ultrasound skills may help in the diagnosis of intra-abdominal bleeding in rural healthcare centers.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".