Associations between number of consecutive night shifts and impairment of neurobehavioral performance during a subsequent simulated night shift
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate sleep and circadian phase in the relationships between neurobehavioral performance and the number of consecutive shifts worked. METHODS: Thirty-four shift workers [20 men, mean age 31.8 (SD 10.9) years] worked 2-7 consecutive night shifts immediately prior to a laboratory-based, simulated night shift. For 7 days prior, participants worked their usual shift sequence, and sleep was assessed with logs and actigraphy. Participants completed a 10-minute auditory psychomotor vigilance task (PVT) at the start (~21:00 hours) and end (~07:00 hours) of the simulated night shift. Mean reaction times (RT), number of lapses and RT distribution was compared between those who worked 2-3 consecutive night shifts versus those who worked 4-7 shifts. RESULTS: Following 4-7 shifts, night shift workers had significantly longer mean RT at the start and end of shift, compared to those who worked 2-3 shifts. The slowest and fastest 10% RT were significantly slower at the start, but not end, of shift among participants who worked 4-7 nights. Those working 4-7 nights also demonstrated a broader RT distribution at the start and end of shift and had significantly slower RT based on cumulative distribution analysis (5 (th), 25 (th), 50 (th), 75 (th)percentiles at the start of shift; 75th percentile at the end of shift). No group differences in sleep parameters were found for 7 days and 24 hours prior to the simulated night shift. CONCLUSION: A greater number of consecutive night shifts has a negative impact on neurobehavioral performance, likely due to cognitive slowing.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".