Nurses’ experiences of caring for people with COVID-19 in Hong Kong: a qualitative enquiry
Bibliographic record
Abstract
OBJECTIVES: Nurses are the largest group of healthcare workers on the front line of efforts to control the COVID-19 pandemic. An understanding of their nursing experiences, the challenges they encountered and the strategies they used to address them may inform efforts to better prepare and support nurses and public health measures when facing a resurgence of COVID-19 or new pandemics. This study aimed to explore the experiences of nurses caring for people with suspected or diagnosed COVID-19 in Hong Kong. DESIGN: A qualitative study was conducted using individual, semistructured interviews. All interviews were audio-recorded and transcribed verbatim for thematic analysis. SETTING: Participants were recruited from acute hospitals and a public health department in Hong Kong from June 2020 to August 2020. PARTICIPANTS: A purposive sample of registered nurses (N=39) caring for people with COVID-19 in Hong Kong were recruited. RESULTS: Two-thirds of the nurses had a master's degree and over a third had 6-10 years of nursing experience. Around 40% of the nurses cared for people with COVID-19 in isolation wards and a quarter performed COVID-19-related work for 31-40 hours/week. Most (90%) had training in COVID-19 and three-quarters had experience of working in infection control teams. Six key themes emerged: confronting resource shortages; changes in usual nursing responsibilities and care modes; maintaining physical and mental health; need for effective and timely responses from relevant local authorities; role of the community in public health protection and management; and advanced pandemic preparedness. CONCLUSIONS: Our study found that nurses possessed resilience, self-care and adaptability when confronting resource shortages, changing nursing protocols, and physical and mental health threats during the COVID-19 pandemic. However, coordinated support from the clinical environment, local authorities and community, and advanced preparedness would likely improve nursing responses to future pandemics.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".