Insufficient sleep and fitness to drive in shift workers: a systematic literature review protocol
Bibliographic record
Abstract
BACKGROUND: The majority of shift workers experience insufficient sleep as a result of their employment. Insufficient sleep is associated with impaired neurocognitive functioning, affecting key skills required for driving, resulting in shift workers experiencing a disproportionate burden of RTC injuries and fatalities. Yet, to our knowledge, no systematic literature review (SLR) exists to critically appraise and synthesise evidence on the determinants of fitness to drive (assessed on-road) and driving performance (assessed in a driving simulator) in shift workers with insufficient sleep. OBJECTIVES: A SLR protocol is established to conduct analysis and synthesis of the level of evidence and confidence in the determinants of fitness to drive and driving performance, among shift workers with insufficient sleep. METHODS: This study follows Cooper and Hedges' established SLR methodology: formulate the problem, locate and select studies, collect data, appraise critically, analyse and present data, interpret results and disseminate information. Critical appraisal and analysis follows the 2017 American Academy of Neurology guidelines determining the level of evidence and the level of confidence for each determinant identified in the literature. Protocol and results reporting adhere to the Preferred Reporting Items for Systematic reviews and Meta-Analyses Protocols guidelines. CONCLUSIONS: This SLR contributes to research evidence examining the impact of insufficient sleep and driver sleepiness on fitness to drive and driving performance. Analysis of the level of evidence and level of confidence in the existing literature will advance evidence-informed prevention strategies and critical decision-making, to mitigate adverse effects of insufficient sleep for improving road safety.
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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 | 0.093 | 0.105 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.057 | 0.009 |
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".