Work-related injuries and attendance at a Canadian regional emergency department
Bibliographic record
Abstract
BACKGROUND: Monitoring trends in the burden of illness and injury attributable to work is key in assessing occupational health hazards; however, New Brunswick does not participate in the Canadian National Ambulatory Care Reporting System which itself does not collect details of occupation and industry. AIMS: We set out to determine the proportion of emergency department attendances that were attributable to a work-related cause. We also wanted to evaluate the recording of occupation in the electronic health record system, and to describe the characteristics of patients with a work-related presentation. METHODS: A retrospective observational study over a 1-year period was conducted using an administrative database obtained from Canadian Emergency Department Information System. Descriptive statistics are used to present the analysis of categorical and continuous data. RESULTS: A total of 49 365 patients were included for analysis. Two per cent of patients presented with a self-reported work-related condition. Health care and social assistance, construction, retail trade and manufacturing were the most common industries reported by patients. CONCLUSIONS: This study found the rate of work-related medical conditions to be substantially less than expected, and that occupation was not captured for any patients presenting to the emergency department with a work-related condition, despite a field being available in the electronic health record registration system. We were able to analyse the industry sectors for work-related presentations. The recording and coding of occupation and industry would significantly benefit occupational epidemiology in emergency medicine as well as potentially improving patient outcomes and health system efficiencies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".