Nurses Leading the Way: A Qualitative Study of Nursing Leadership, Innovation and Opportunity in Primary Care During a Public Health Crisis
Bibliographic record
Abstract
Introduction: Nurses in primary care play critical roles during public health crises; however, nursing leadership was underutilized during the COVID-19 response. This study explores nurses' leadership roles during the pandemic and their perspectives on the value of nursing leadership in primary care. Methodology: We conducted qualitative interviews with 76 nurses across four Canadian regions. Participants described their roles and the barriers and facilitators encountered during the COVID-19 pandemic. We used thematic analysis and examined themes relevant to leadership. Results: Three themes emerged: actualizing leadership, leveraging leadership experience and the value of nursing leadership. Nurses demonstrated leadership competencies, including educating teams and developing care delivery strategies. Participants emphasized the importance of involving nursing leadership in decision making and policy development. Conclusion: Sustaining and leveraging nursing leadership post-pandemic is essential to enhance collaboration and strengthen healthcare systems. Involving nurses in decision making can address system challenges and improve responses to future public health crises.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".