Work injury risk by time of day in two population-based data sources
Bibliographic record
Abstract
OBJECTIVE: To estimate the rate of work injury over the 24 h clock in Ontario workers over 5 years (2004-2008). METHODS: A cross-sectional, observational study of work-related injury and illness was conducted for a population of occupationally active adults using two independent data sources (lost-time compensation claims and emergency department encounter records). Hours worked annually by the Ontario labour force by time of day, age, gender and occupation were estimated from population-based surveys. RESULTS: There was an approximately 40% higher incidence of emergency department visits for work-related conditions than of lost-time workers' compensation claims (707 933 emergency department records and 457 141 lost-time claims). For men and women and across all age groups, there was an elevated risk of work-related injury or illness in the evening, night and early morning periods in both administrative data sources. This elevated risk was consistently observed across manual, mixed and non-manual occupational groups. The fraction of lost-time compensation claims that can be attributed to elevated risk of work injury in evening or night work schedules is 12.5% for women and 5.8% for men. CONCLUSIONS: Despite the high prevalence of employment in non-daytime work schedules in developed economies, the work injury hazards associated with evening and night schedules remain relatively invisible. This study has demonstrated the feasibility of using administrative data sources to enhance capacity to conduct surveillance of work injury risk by time of day. More sophisticated aetiological research is needed to understand the specific mechanisms of hazards associated with non-regular work hours.
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 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".