Crisis response to COVID-19: Elements for a successful virtual event transition
Bibliographic record
Abstract
Rapid onset of the COVID-19 pandemic necessitated a crisis response among academic institutions to provide continuity of learning, in an alternate structure, as on-ground campuses across the country closed. This led to a myriad of virtual and online learning formats for collegiate programs. Ironically, it also altered plans among existing online programs scheduled for in-person, on-campus residency requirements. Complying with newly imposed institution regulations, a small private university in southwestern Pennsylvania was required to move their traditional on-ground Doctor of Nursing Practice residency to a virtual platform. Leveraging online tools and creating a new format was needed to effectively meet program requirements. Success of the residency was dependent upon a straightforward information technology program with adequate support and detailed student resources. Residency structure from the existing on-ground program was combined with online tools to successfully adapt the event into a virtual format. Feedback provided by students and faculty was reviewed to streamline and improve future transitions. The advent of COVID-19 created an opportunity for the nursing program to learn how to transition a key educational on-ground event to a successful virtual one. Although crisis response is common in the clinical setting, adapting to meet critical needs is also essential to the academic environment. Rapid response with forming a virtual residency has provided a foundation for continued growth and refinement of on-ground events being moved to an online platform during a time of crisis. Critical elements for transitioning to the virtual environment are summarized.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".