Subjective and Objective Evaluation of Sleep and Performance in Daytime Versus Nighttime Sleep in Extended-Hours Shift-Workers at an Underground Mine
Bibliographic record
Abstract
Extended hours of shift work has the potential for adverse consequences for workers, particularly during the nightshift, such as poorer sleep quality during the day, increased worker fatigue, and fatigue-related accidents and decreased work performance. This study examined subjective and objective measurements of sleep and performance in a group of underground miners before and after the change from a backward-rotating 8-hour to a forward-rotating 10-hour shift schedule. The purpose of this study was to evaluate the short- and long-term impact of a shift schedule change on sleep and performance. The results demonstrated improved subjective and objective measures of sleep and performance on the new 10-hour nightshift schedule. The 10-hour nightshift workers subjectively reported more refreshing sleep, fewer performance impairments and driving difficulties than 8-hour nightshift workers. The results of the objective measures of sleep and performance on the 10-hour nightshifts were overall similar or possibly better than those measured on the 10-hour dayshifts. These are some of the first data to suggest that a nightshift that does not encompass the entire night period could have significant benefits to shift-workers. We suggest that these benefits are mostly the result of the timing of the new nightshift start and end times rather than other shift-schedule factors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".