Using technology to bridge the gap for remote healthcare education during COVID-19
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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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".