Switching to Daylight Saving Time and work injuries
Bibliographic record
Abstract
Objective To examine whether switching to and from Daylight Saving Time (DST)d1 h shift forward in the spring and 1 h shift back in the autumndis associated with an increase in work injuries. Method Data on work-related injuries were obtained from compensation claim records from the Ontario Workplace Safety & Insurance Board for the period 1993e2007. A Poisson regression model was run separately comparing the number of no lost time claims and lost time claims during the week of DST change with the week following DST change, and the week preceding DST change. We also examined if differences in the relationship between DST and work injury claims were present across industry, age, gender and job tenure groups. Results The results of our regression model did not show an increase in the incidence of work injury claims in the days immediately following the spring shift to DST. There was a significant decrease in the number of claims on Thursday, Friday and Saturday following the spring transition to DST. However, this decline was solely due to the years when Good Friday occurred during DST week (1993, 1998 and 2004) when fewer people are at work. For the autumn transition from DST, no evidence was found that the gain of 1 h sleep results in a decrease or increase in work injury claims. Conclusion Our findings show that the shift to and from DST had no detrimental effects on the incidence of claims for work injuries in Ontario, Canada.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".