Rural Home Care Nursing During COVID-19
Bibliographic record
Abstract
BACKGROUND: The SARS-CoV-2 (COVID-19) pandemic continues to be a challenging time for the nursing profession globally. Research indicates that the care of patients with COVID-19 has caused significant psychological stress for nurses. Although much of the world's attention has been on nurses working in emergency departments and intensive care units, the pandemic also posed significant challenges for nurses providing home care services in rural communities. PURPOSE: The purpose of this work was to describe the experiences of rural Canadian home care nurses during the early stages of the COVID-19 pandemic. METHODS: The data for this analysis was derived from a study that explored the continuing education needs of rural home care nurses. Since the data collection for the primary objective occurred in the early stages of the COVID-19 pandemic, COVID-19 related themes were created using interpretive description methodology. Snowball and purposive sampling were used to recruit rural home care registered nurses who were employed in the central and southern areas of a western Canadian province. RESULTS: Six themes were constructed from the data including: Nurses Must Work, Constant State of Flux, Threatened Safety, Loss of Learning Opportunities, Fearing the Unknown, and Hindsight is Easy. CONCLUSION: The experiences of rural home care nurses during COVID-19 reflects the chaos, uncertainty, and fear that was felt globally. Based on the findings of this study, recommendations for future pandemic planning are suggested.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".