Adaptation to Night Shifts and Synchronisation Processes of Night Workers
Bibliographic record
Abstract
Human beings are accustomed to being active and awake during the day, and asleep and rest at night. Since we live in a society which is organised predominantly along daytime activity, therefore working in the night shift may deeply disrupt our social and family life. It is also a well-known fact that night shift causes fatigue and circadian disruption. The basic manifestation of fatigue and circadian rhythm has been linked to health and safety problems, involving decrements in psychophysical and physiological functions, plus subjective complaints. In this context quantitative relationships between shift work and circadian rhythm need to be assessed to explore suitable time schedule, and to minimise sleep depth and fatigue. There is also a great need to discuss circadian disruption, sleepiness and the increasing cost of work related illness among night workers. In this regard, some aspects of fatigue and circadian disruption caused from night shift work are revealed in this paper aiming to increase workers' health, safety and well being as well as productivity. Light/dark cycle and social stimuli issues acting on the circadian timing systems are also explored to solicit opinions and discussion on the controversy of night work. Suggestions are therefore likewise given to enhance workers' adaptation to night shift and synchronization process.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".