Alertness After Night Shifts Among Workers in the Aluminium Industry
Bibliographic record
Abstract
OBJECTIVE: This case-crossover study aimed to evaluate changes in alertness following night shifts among workers in the aluminium industry, and the potential impact of the number of consecutive night shifts and shift length. METHODS: We estimated alertness on 87 aluminium workers by a 3-minute version of the Psychomotor Vigilance Test after both day and night shifts. Linear mixed models were used for statistical analysis. RESULTS: The level of alertness was significantly lower after three and four consecutive night shifts, compared with after a day shift. No significant differences in alertness were observed between three and four consecutive night shifts, nor between periods of three consecutive 8 + 8 + 8-hour versus 8 + 12 + 12-hour night shifts. CONCLUSIONS: Our findings indicate reduced alertness after three and four consecutive night shifts, compared with after day work. No significant dose-response effect was observed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".