Toward COVID‐19 recovery: Advanced practice nurse leadership in rural Vermont
Bibliographic record
Abstract
AIM: To reflect on the inclusion of an advanced practice nurse (APN) on a clinical leadership team in rural Vermont during the COVID-19 pandemic. BACKGROUND: During the COVID-19 pandemic, APNs contributed to the reimagining of healthcare delivery. In response to pandemic-related organizational needs, one rural health center in Vermont promoted an APN to a leadership position. SOURCES OF EVIDENCE: This critical reflection describes the experience of one APN promoted to a clinical leadership role during the COVID-19 pandemic in rural Vermont in the United States. We use the four stages of crisis (escalation, emergency, recovery, and resolution) and the healthcare leadership framework proposed by Geerts et al. (2021) to consider how APN leaders can contribute in the "recovery stage" of the pandemic. DISCUSSION: APNs who took on leadership roles during the pandemic may have had fewer opportunities to participate in formal leadership development. However, in the case of our rural health center, an APN was able to seek out mentorship, address operational challenges, and provide representation for advanced practice providers. CONCLUSION: This article contributes to the literature on APN leadership during the COVID-19 pandemic, by describing a leadership opportunity that helped build APN leadership capability and capacity in our organization. IMPLICATIONS FOR NURSING PRACTICE: APNs offer a valuable perspective on health leadership teams. As organizations move toward the recovery stage of the pandemic, different leadership styles and skills may be required. IMPLICATIONS FOR HEALTH POLICY: The COVID-19 pandemic provided unexpected leadership opportunities for APNs. Healthcare organizations now have opportunities to reimagine clinical leadership in ways that include APNs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".