Redeployment Among Primary Care Nurses During the COVID-19 Pandemic: A Qualitative Study
Bibliographic record
Abstract
Introduction: Throughout the COVID-19 pandemic, primary care nurses were often redeployed to areas outside of primary care to mitigate staffing shortages. Despite this, there is a scarcity of literature describing their perceptions of and experiences with redeployment during the pandemic. Objectives: This paper aims to: 1) describe the perspectives of primary care nurses with respect to redeployment, 2) discuss the opportunities/challenges associated with redeployment of primary care nurses, and 3) examine the nature (e.g., settings, activities) of redeployment by primary care nurses during the COVID-19 pandemic. Methods: In this qualitative study, semi-structured interviews were conducted with primary care nurses (i.e., Nurse Practitioners, Registered Nurses, and Licensed/Registered Practical Nurses), from four regions in Canada. These include the Interior, Island, and Vancouver Coastal Health regions in British Columbia; Ontario Health West region in Ontario; the province of Nova Scotia; and the province of Newfoundland and Labrador. Data related to redeployment were analyzed thematically. Results: Three overarching themes related to redeployment during the COVID-19 pandemic were identified: (1) Call to redeployment, (2) Redeployment as an opportunity/challenge, and (3) Scope of practice during redeployment. Primary care nurses across all regulatory designations reported variation in the process of redeployment within their jurisdiction (e.g., communication, policies/legislation), different opportunities and challenges that resulted from redeployment (e.g., scheduling flexibility, workload implications), and scope of practice implications (e.g., perceived threat to nursing license). The majority of nurses discussed experiences with redeployment being voluntary in nature, rather than mandated. Conclusions: Redeployment is a useful workforce strategy during public health emergencies; however, it requires a structured process and a decision-making approach that explicitly involves healthcare providers affected by redeployment. Primary care nurses ought only to be redeployed after other options are considered and arrangements made for the care of patients in their original practice area.
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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 | 0.011 | 0.015 |
| 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 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".