Nurses' drowsy driving prevention strategies: A qualitative exploratory multiple-case study
Bibliographic record
Abstract
Objective: To explore the strategies registered and licensed practical nurses implemented to lessen the possibility of driving while drowsy after working nights in hospitals, nursing homes, and home health facilities. A review of literature indicated shift work, circadian rhythm interruptions and multiple stressors such as age, caring for children/aging parents and working multiple jobs may affect drowsy driving. Studies on global drowsy driving and cultural differences may affect international applicability. Further research was needed to better understand drowsy driving best practices, training modalities for health care workers, and developing a multidisciplinary collaboration between management and night-shift workers.Methods: A qualitative, exploratory multiple-case method was utilized. Registered and licensed practical nurses (N = 12) were interviewed.Results: Identified themes including three major themes emerged from the data analysis. 1) Fatigue is a significant challenge that impedes driving home safely. 2) Night nurses experience significant additional stressors relating to caring for family, school, and multiple jobs. 3) Multiple strategies are helpful, but they do not replace the body’s need for sleep. Strategies for managing drowsy driving and anxiety/stress producing issues were offered.Conclusions: Twelve-night shift workers shared the challenges drowsy driving and anxiety/stress producing issues outside of the work-related duties. Health care administrators may use the results to gain insight for training nurses for the night shift to prevent drowsy driving injuries and fatalities. The results of the study may offer a platform for further investigation that may uncover best-practice strategies for health care administrators staffing other types of 24-hour medical care facilities.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".