Safety implications of different forms of understaffing among nurses during the COVID‐19 pandemic
Bibliographic record
Abstract
AIM: The aim of this study was to investigate the processes through which personnel understaffing and expertise understaffing jointly shape near misses among nurses during the COVID-19 pandemic. BACKGROUND: Inadequate staffing is a chronic issue within the nursing profession, with the safety consequences of understaffing likely being exacerbated by the COVID-19 pandemic. DESIGN: This study used a three-wave, time-separated survey design and collected data from 120 nurses in the United States working on the frontline of the pandemic in hospital settings. METHODS: Participants were recruited through convenience sampling in early April 2020. Eligible nurses completed three surveys across a 6-week period during the COVID-19 pandemic from mid-April to the end of May 2020. Study hypotheses were tested with path analyses. RESULTS/FINDINGS: Results reveal that personnel understaffing and expertise understaffing jointly shape near misses, which are known to precede and contribute to accidents and injuries, through different mechanisms. Specifically, personnel understaffing led to greater use of safety workarounds, which only induced near misses when cognitive failures were high. Further, higher levels of cognitive failures appeared to be the result of greater expertise understaffing. CONCLUSION: This study highlights the importance of addressing issues of understaffing, especially during times of crisis, to better promote nurse and patient safety. IMPACT: This study was the first to examine the distinct mechanisms by which two forms of understaffing impact safety outcomes in the form of near misses. Understanding these mechanisms can help leaders and policymakers make informed staffing decisions by considering the safety implications of understaffing issues.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".