Leveraging technology to prepare for a successful Magnet® virtual site visit
Bibliographic record
Abstract
Magnet Site Visits are part of the comprehensive review process for organizations seeking the American Nurses Credentialing Center’s (ANCC) prestigious Magnet® recognition for nursing excellence. In 2021, the ANCC’s Magnet Recognition Program® began offering the option for an onsite or Virtual Site Visit (VSV) for domestic and international organizations. These visits are the culmination of years of work that showcase the best in nursing practice and interprofessional collaboration. Chief Nursing Officers (CNOs) and Magnet Program Directors (MPDs) collaborate to strategically plan these visits. This article will describe a Magnet® recognized community hospital’s preparation for a VSV during the height of the COVID-19 pandemic. It will include development of partnerships with Information Technology Specialists, planning and logistics for meetings, development and implementation of a comprehensive education plan, and the overall orchestration of a VSV. Strategies will address the engagement of nurses and leaders amidst the challenges of the COVID-19 pandemic. With strategic planning, effective change management, and the adoption of technology, MPDs may gain confidence to facilitate a Magnet Site Visit virtually or in-person.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.013 |
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".