Using technology to bridge the gap for remote healthcare education during COVID-19
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
The COVID-19 pandemic has initiated profound changes to the delivery of healthcare education. With traditional in-person instruction, learners are at risk of acquiring and spreading the virus to others. Therefore, alternative strategies for immediate, effective and safe continuation of healthcare education are needed. To support this transition, technologies previously considered for the sake of novelty may now be reconsidered as technologies for the sake of necessity. Rather than reinventing content and setting up individual infrastructure for delivery, we can capitalise on existing momentum in innovation to facilitate remote education while saving resources for other urgent efforts. Incorporating fidelity in healthcare education will allow us to effectively continue training and assessment of healthcare professionals through safe-distanced approaches. Technology has played an invaluable role in our response to the diverse challenges presented by the COVID-19 pandemic. For example, as demand temporarily outstripped existing production capacities, 3D-printing quickly scaled and addressed widespread shortages of face shields or nasal swabs on commercial and grass-roots levels. Videoconferencing platforms were adapted for webinars, student lectures or remote doctor consultations. Similarly, fidelity enhanced learning is primed to support healthcare education during COVID-19 for students and advanced healthcare professionals alike. In anticipation of a potential second wave of COVID-19, medical schools have chosen remote online instruction for the fall semester and possibly beyond, which will result in medical schools using considerable resources to hastily …
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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