Effectiveness of bright light exposure, modafinil and armodafinil for improving alertness during working time among nurses on the night shift: A systematic review
Bibliographic record
Abstract
Objective We aimed to evaluate the effectiveness of bright light exposure, modafinil, and armodafinil for improving alertness during working time among nurses on the night shift.Methods We carried out a literature search using the PubMed, Scopus, and Web of Science electronic databases regarding articles pertaining to workplace interventions for improving wakefulness among nurses working the night shift using the following medical subject headings: ((((Bright light exposure) OR wakefulness medications) AND Nurses AND Night Shift-work)).Results The searches generated a total of 34 records on the PubMed database, 130 on the Scopus database, and 32 on the Web of Science database. A total of 95 studies were identified after removal of duplicates. Nevertheless, the 95 articles were screened, 75 studies were excluded based on the review of titles and abstracts, and further 15 full-text articles were excluded because the studies did not meet the selection criteria. A total of 632 subjects from 5 studies were included.Conclusion Bright light exposure is beneficial in improving alertness during the night shift. On the other hand, armodafinil or modafinil taken before the commencement of night shift work is effective in the treatment of excessive sleepiness associated with shift work sleep disorder.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".