Response and innovations of advanced practice nurses during the COVID‐19 pandemic: A scoping review
Bibliographic record
Abstract
AIM: Identify and map international evidence regarding innovations led by or involving advanced practice nurses in response to COVID-19. BACKGROUND: COVID-19 necessitated unprecedented innovation in the organization and delivery of healthcare. Although advanced practice nurses have played a pivotal role during the pandemic, evidence of their contributions to innovations has not been synthesized. Evidence is needed to inform policies, practices, and research about the optimal use of advanced practice nurses. METHODS: A scoping review was conducted and reported using the PRISMA-ScR checklist. Electronic databases were searched for peer-reviewed articles published between January 2020 and December 2021. Papers were included that focused on innovations emerging in response to COVID-19 and involved advanced practice nurses. RESULTS: Fifty-one articles were included. Four themes were identified including telehealth, supporting and transforming care, multifaceted approaches, and provider education. Half of the articles used brief and mostly noncomparative approaches to evaluate innovations. CONCLUSION: This is the first synthesis of international evidence examining the contributions of advanced practice nurses during the pandemic. Advanced practice nurses provided leadership for the innovation needed to rapidly respond to healthcare needs resulting from COVID-19. Innovations challenged legislative restrictions on practice, enabled implementation of telehealth and new models of care, and promoted evidence-informed and patient-centered care. IMPLICATIONS FOR PRACTICE: Advanced practice nurses led, designed, implemented, and evaluated innovations in response to COVID-19. They facilitated the use of telehealth, supported or transformed models of care, and enabled health providers through education, mentorship, and mental health support. IMPLICATION FOR POLICY: Advanced practice nurses are a critical resource for innovation and health system improvement. Permanent removal of legislative and regulatory barriers to their full scope of practice is needed.
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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 | 0.024 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".