Fatigue and shift work
Bibliographic record
Abstract
Shift work is a ubiquitous phenomenon and its adverse effects on workers' physical and mental health have been documented. In the sleep literature, differentiating between the symptoms of fatigue and sleepiness, and developing appropriate objective and subjective measures, have become very important endeavors. From such research, fatigue and sleepiness have been shown to be distinct and independent phenomena. However, it is not known whether shift work differentially affects fatigue and sleepiness. In an attempt to answer this question, 489 workers from a major Ontario employer completed a series of subjective, self-report questionnaires, including the Fatigue Severity Scale (FSS) and the Epworth Sleepiness Scale. Workers were separated into four groups based on the frequency with which they are engaged in shift work (never, fewer than four times per month, 1-2 days per week, 3 days or more per week). The frequency of shift work was found to have a significant effect on subjective fatigue, but not on subjective sleepiness. Compared with the subjects who never had a shift schedule, those who worked in a shift for 3 days or more had significantly higher mean score of the FSS. In agreement with previous results, a low correlation was found between workers' subjective fatigue and sleepiness scores, providing further support for the concept of fatigue and sleepiness as distinct and independent phenomena. Future research should address the possibility of using the FSS as an indicator when the frequency of shift work has become high enough to adversely affect work performance or cause health problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".