Potential Dangers of Nursing Overtime in Critical Care
Bibliographic record
Abstract
Around the world, registered nurses are working increasing amounts of overtime. This is particularly true in critical care environments, which experience unpredictable fluctuations in patient volume and acuity combined with a need for greater numbers of specialized nurses. Although it is commonplace, little consensus exists surrounding the effects of overtime on nursing sick time and patient outcomes. Using data from 11 different critical care units nestled within three major academic health science centres in Southern Ontario, a multilevel-model Poisson regression analysis was used to evaluate the association between nursing overtime and nursing sick time, patient mortality and patient infection incidents. Most significantly, for every 10 hours of nursing overtime worked, study findings revealed an associated 3.3-hour increase in nursing sick time. Because of the potential cost and patient care ramifications, hospitals and nurse managers are encouraged to track collective and individual paid and unpaid hours to impose appropriate limits and ensure accountability. Further qualitative research should be commissioned to explore the underlying reasons for these findings and diversify the settings and, in turn, wider application.
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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 | 0.011 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| 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".