Redeployment to COVID-19 Workforces: The Lived Experiences of Public Health Nurses
Bibliographic record
Abstract
The COVID-19 pandemic has placed unprecedented strain on healthcare systems globally. Public health nurses (PHNs) have been especially impacted as many transitioned from non-clinical roles to the frontlines of the pandemic. Available research focuses predominately on redeployed healthcare providers within hospital-based environments, including critical care areas, with limited research within public health settings. The purpose of this study was to explore the lived experiences of PHNs redeployed to COVID-19 response.\nEight PHNs were recruited from two local public health units in southeastern Ontario using purposive sampling until data saturation was achieved. Semi-structured interviews were conducted, audio recorded and transcribed. Interview data were analyzed in NVivo using Interpretative Phenomenological Analysis. Transcriptions were systematically analyzed individually and then compared across different interviews to identify common themes and subthemes. The novelty of the COVID-19 pandemic presented as a storm for PHNs creating a host of unknowns and stressful environments. Participants shared their experiences of weathering the Storm, a prolonged pandemic response which led to unsustainable workplaces, personal obligations and stressors, and different levels of burnout. As the storm began to settle, the participants were left dealing with the aftermath of the storm, adapting to new practice environments and relearning pre-pandemic roles. Finally, participants shared reflections on a storm to remember, which comprised opportunities for personal growth, community connection, and leadership.\nUnderstanding the lived experiences of PHNs is critical to informing nursing practice and preparing for future pandemics at a health systems level. Knowledge from PHNs’ experiences can inform organizational policy on evidence-based staffing models, continuing education, and training for PHNs on an annual basis and facilitate management practice improvements including strength-based approaches to redeployment, and promotion of psychologically safe work environments. PHNs’ experiences must be considered in future public health emergencies to better support PHNs’ redeployment, prevent burnout, and protect communities in future public health emergencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".