Using POV Wearable Technology as a Tool in Virtual Teaching Sessions to Supplement Clinical Skills Training: a Medical Student Perspective
Bibliographic record
Abstract
The COVID-19 pandemic has led to many undergraduate medical programs shifting their preclinical curricula online— reducing access to clinical skills sessions and ultimately causing gaps in students’ knowledge. This study sought to better understand the impact and role of virtual clinical skills training sessions, using point-of-view (POV) livestreaming wearable technology, in supplementing medical students’ learning. 38 University of Ottawa medical students were recruited to participate in a 1.5-hour virtual clinical teaching session. An abdominal physical examination was broadcasted through two views (chest-mounted smartphone, room overview). Participants completed pre- and post-event questionnaires on their overall impression, satisfaction/challenges, and platform efficacy compared to other learning modalities. Improvements were noted in participants’ perspectives towards event engagement (p=0.042, Cohen’s d=0.48), comparability to in-person encounters (p=<0.001, Cohen’s d=0.75), and confidence performing a physical exam (p=<0.001, Cohen’s d=1.35). Participants found events were relevant to curriculum objectives (4.55±0.69), engaging and interactive (4.50±0.65), and reported good visualization (4.61±0.59). All participants were interested in attending a subsequent event. Virtual clinical skill teaching sessions using POV technology were enjoyable and helpful in combating current COVID-related gaps in medical education, adding to the growing literature on the beneficial role of innovative virtual learning opportunities within the medical school curricula. Future research should look to evaluate the use of POV wearable technology in settings beyond the classroom.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".