Understanding the Second Year of the <scp>COVID</scp> ‐19 Pandemic From a Nursing Perspective: A Multi‐Country Descriptive Study
Bibliographic record
Abstract
AIM(S): To determine common and distinct factors experienced by nurses working in acute care settings during the second year of the COVID-19 pandemic. DESIGN: An online qualitative descriptive study with eight open-ended questions and a comprehensive demographic profile administered via the Qualtrics XM survey software. METHODS: Thirteen countries formed teams and led online data collection in their respective countries through various approaches. The data collection period occurred between January 1, 2021, and February 28, 2022. Descriptive thematic analysis was conducted in English (with translation), Spanish, and Korean to analyse the qualitative data. Descriptive statistics summarised the responses to the demographic profile. RESULTS: Worldwide, a final sample size of n = 1814 produced 6483 qualitative data points for analysis. The results identified ongoing occupational risk factors for nurses during the pandemic's second year, including mental health issues, yet showed some improvements in access to personal protective equipment and resources. Four themes emerged from the qualitative analysis, highlighting role changes, living states, and insights into the implementation of pandemic response measures. CONCLUSION: Despite individual occupational risks nurses described, structural factors associated with healthcare delivery produced common nursing experiences during the pandemic. Additionally, at least two distinct stages of pandemic response implementation were demarcated by treatment availability (e.g., vaccine development). IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: There is potential for common pandemic response policies for nurses, centered on specific factors, such as the increased provision of mental health support services by healthcare organisations. IMPACT: This study helped determine the common and distinct work experiences during the second year of the COVID-19 pandemic. Nurses simultaneously experienced increased workload, role changes, perpetual fear and fatigue, daily hostility, and chaos in the implementation of pandemic responses. The results will impact nurses and those they serve along with future pandemic response policies. REPORTING METHOD: We have adhered to the SRQR reporting guidelines. PATIENT OR PUBLIC CONTRIBUTION: This study did not include patient or public involvement in its design, conduct, or reporting.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".