Perspectives of primary care nurses on the organization of the COVID-19 vaccine rollout: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Primary care nurses, including nurse practitioners (NPs), registered nurses (RNs), and licensed practical nurses/registered practical nurses (LPNs/RPNs), play a pivotal role in pandemic management and outbreak planning. There is extensive literature surrounding COVID-19 vaccination efforts in Canada; however, limited research addresses the involvement of primary care nurses, as well as the organization and integration of these efforts into primary care settings. This study aimed to describe the organizational challenges, barriers, and facilitators to primary care nurses' roles in COVID-19 vaccination. METHODS: As part of a mixed methods case study, we conducted semi-structured qualitative interviews with primary care nurses employed in regions across four Canadian provinces: British Columbia, Ontario, Nova Scotia, and Newfoundland and Labrador. During the interviews, nurses described their activities throughout different phases of the COVID-19 pandemic, factors that facilitated or impeded their efforts, and potential contributions nurses could have made. We applied a thematic analysis approach and analyzed codes related to the organization of the COVID-19 vaccination rollout. RESULTS: We interviewed 76 nurses (24 NPs, 37 RNs, and 15 LPNs/RPNs) between May 2022 and January 2023. We identified five overarching components of the COVID-19 vaccination rollout that influenced primary care nurses' perceptions and experiences: (1) information, (2) training, (3) coordination, (4) integration, and (5) compensation. Participants reported both positive and negative experiences with the vaccine rollout. Rapidly evolving information made it difficult for nurses to stay informed and training for vaccine delivery posed barriers due to time requirements and redundancy. Support was often lacking for new electronic systems, and regional coordination varied, sometimes resulting in miscommunication. Delays in integrating vaccination into primary care, logistical challenges, and disparities in compensation between nurses and physicians also presented challenges. CONCLUSIONS: Findings highlight the critical roles of primary care nurses in mass vaccination campaigns, underscoring the need for targeted information, effective training, streamlined coordination, better integration into primary care, and more equitable compensation. Integrating these services into primary care can enhance future vaccination efforts by leveraging nurses' expertise to improve vaccine access and delivery.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".