“It Takes a Virtual Village” Achieving Magnet Redesignation Amidst the COVID-19 Pandemic
Bibliographic record
Abstract
This article outlines how a Canadian hospital achieved the American Nursing Credentialing Center Magnet Recognition Program redesignation after participating in a virtual site visit (VSV) appraisal process amidst the COVID-19 pandemic. Within our current COVID-19 landscape, being a resilient Magnet-designated organization is paramount. In this context, the American Nurses Credentialing Center (ANCC) has developed a VSV model that (1) extends the use of audio/video (A/V) conferencing technology to showcase nursing excellence; (2) maintains the integrity of the appraisal process; and (3) ensures the safety and well-being of staff, patients and their care partners, and the appraisers. Key narrative insights are highlighted around planning and on-site execution of a successful VSV. The redesignation is a culmination of several stakeholders' efforts who shared their sense of pride, inspiration, and accomplishment during the VSV. The redesignation status notification exemplifies resiliency and was welcomed amidst uncertainty with the evolving COVID-19 pandemic. The planning and on-site implementation plan may serve as a blueprint for others who will be engaged in a VSV as part of their designation or redesignation journey. Insights are shared around preparing for the VSV, hosting the VSV, and achieving the ANCC Magnet Recognition Program redesignation.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.014 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".