Sleep, Sleepiness, and Driving Performance in Healthcare Shiftworkers
Bibliographic record
Abstract
Almost half of Canadian healthcare workers are employed in rotating day/night shifts to provide essential 24-hour services. However, poor sleep quantity and quality are pervasive in shiftwork and may negatively impact cognitive functions required for driving. Furthermore, healthcare shiftworkers (HCSW) are under-represented in research examining shiftworkers’ sleep and driving performance. Thus, this dissertation examined sleep, sleepiness and driving performance in HCSW via three aims. First, chapter 2 aimed to quantify and describe HCSWs’ sleep-related driving experiences and advance the understanding of occupational adaptations used by HCSWs to meet driving demands. Second, chapter 3 aimed to quantify differences in sleep, sleepiness, and driving performance outcomes between HCSW and dayworkers (DW). Finally, chapter 4 aimed to identify whether demographic, sleep- or driving-related outcomes may predict the 6-week sum of adverse driving events reported.\nChapter 2 findings show that the majority of HCSWs experienced insufficient sleep during their workweek and reported elevated rates of risky and sleep-related driving behaviours. Despite recurring, multi-layered adaptations to mitigate the risk of sleep-related driving events, 90% of HCSW reported at least sleep-related driving events in the past year, described as a routine and predictable consequence of shiftwork. Limited training, resources, and unpredictable scheduling were highlighted as systemic barriers disproportionately affecting younger HCSW. Chapter 3 findings show that HCSW (versus DW) experience significantly lower sleep quantity and quantity, and more frequent occurrences of severely insufficient sleep below thresholds indicated for impaired driving. Further, HCSW reported higher ratings of subjective sleepiness and higher occurrences of sleep-related and adverse driving events. Chapter 4 findings identified shiftwork, younger age, higher scores on the Pittsburg Sleep Quality Index, and more frequent occurrences of sleep <6h/24h as factors significantly predicted a higher 6-week sum of adverse driving events. Overall, findings suggest that HCSW are an at-risk group of drivers, with important implications in future research, healthcare worker education, and policy, with a crucial need to focus on younger workers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".