A feasible intervention to reduce fatigue in rapidly rotating shift nurses
Bibliographic record
Abstract
Nearly half of nurses in Canadian hospitals work rotating shifts. Although necessary, shift work is associated with various short- and long-term health consequences such as cancer, cardiovascular disease, and gastrointestinal problems. These negative effects are partly caused by circadian misalignment (i.e., when one’s body clock is not aligned with the demands of the environment). While previous research has found that controlling light exposure can improve circadian alignment in individuals working permanent night shifts, light-based interventions are rarely adapted for and tested in rapidly rotating shift workers. In this field study, we developed and tested a light-based intervention to reduce fatigue, errors, and sleepiness in rapidly rotating shift nurses. A total of 33 nurses completed daily self-report measures during two separate weeks: they followed their usual routine during the first week and completed the intervention during the second week. The intervention involved 40 minutes of light exposure from a portable light box before night shifts, light avoidance using sunglasses after night shifts, and suggestions regarding the ideal times to sleep and nap. As predicted, nurses complied with the intervention and experienced reductions in fatigue and work-related errors during the intervention phase. However, their sleepiness was unaffected. Although more controlled studies are needed to assess the long-term efficacy of this intervention, our results suggest that such feasible and cost-effective interventions may help minimize some of the adverse effects associated with working rapidly rotating shifts
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".