Impact of the COVID-19 pandemic on clinical care and patient-focused outcomes of advanced nursing practice: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has significantly impacted advanced practice nurses' practice and posed great challenges in patient care delivery. PURPOSE: The aim of this study was to investigate the impact of the COVID-19 pandemic on the practice of advanced practice nurses in mainland China and Hong Kong Special Administrative Region (SAR). Methods A cross-sectional descriptive survey was conducted March 2021 and January 2022. Advanced practice nurses were invited to participate in an online survey. The questionnaire described the socio-demographic characteristics, the impact of the COVID-19 pandemic on advanced nursing practice, patient outcomes, education needs about COVID-19, and the challenges, support, and concerns related to the advanced practice nurse practice during the pandemic. Wilcoxon signed-rank test or McNemar test were applied to measure the practice of APNs before and during the COVID-19 pandemic. RESULTS: Respondents (N = 336) were from mainland China (n = 234) and Hong Kong SAR (n = 102). Participants reported increased practice-related workload during the pandemic. The proportions of advanced practice nurses focused on disease prevention (36.9%) and psychosocial well-being (15.5%) for patient-focused outcomes during the pandemic were higher compared to before the pandemic. Key challenges and concerns during the pandemic included heavy workloads and health concerns for themselves and their families. Despite difficulties, there were reports of positive changes since the outbreak including implementation of innovative measures to facilitate the advanced practice nursing and education about COVID-19. CONCLUSION: The study findings highlight that advanced practice nurses' work and responsibilities have changed in response to the pandemic. Providing education about COVID-19, innovative measures to facilitate advanced practice nursing, and understanding advanced practice nurses' concerns and challenges in providing patient care may inform future developments for improving their professional practice.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".