Facilitators, Barriers, Conditions, and Recommendations of Pediatric Nurses Reassigned to Adult Care During COVID-19 Pandemic: A Mixed Methods Study
Bibliographic record
Abstract
Aims: To describe the conditions, facilitators, barriers, and recommendations of pediatric nurses reassigned to adult care during the COVID-19 pandemic. Background: During COVID-19 pandemic, nurses who were certified, received advanced education, and with years of experience in their fields were asked to, or mandated to, care for adult patients, often with COVID-19 and in intensive care units. Introduction: Nurses struggled with this transition worldwide. Literature searches reflected no guidance in specialty reassignment during a pandemic. Methods: International, mixed method, convergent study using GRAMMS Mixed Methods framework. Two instruments disseminated internationally during 2021 on Survey Monkey© in three languages: (1) Researcher-developed, validated, feelings, beliefs, circumstances, and recommendations survey, (2) Medical-surgical skills survey from Canadian Association of Schools of Nursing. Integrated quantitative and qualitative design for investigatory depth. Analysis using SocialSciences calculator© and Narrative Inquiry methodology. Meleis's Transitions theory provided framework. Results: A total of 122 nurses from 12 countries responded. Sixty-four percent of pediatric nurses reported they were mandated to work with adults with no choice; 64% received no preparation for change in specialty. Respondents reported suffering, absence of training to care for adults, lack of COVID-19 related skills such as care of ventilated patient, proning, mental health support, palliative care, or comforting families of the dying. Ninety-two percent recommended improvement, advising advance planning for transition in assignments, making transitions smoother, and diminishing number of transitions. National and international policies on floating supported the participants’ recommendations. Discussion: Study reports challenges experienced by nurses reassigned outside of their specialties without training or preparation and offers critical recommendations to ensure both nurse and patient safety during future disasters or pandemics. Conclusion/Implications Nursing and Health Policy: Planning for quality nursing care during transitions between specialties is possible. Staff nurses must be included, trained, and prepared. Literature on floating provides solid background for moving nurses between specialties.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".