Workers’ compensation claims for COVID-19 among workers in healthcare and other industries during 2020–2022, Victoria, Australia
Bibliographic record
Abstract
OBJECTIVE: To identify and characterise COVID-19 workers' compensation claims in healthcare and other industries during the pandemic in Victoria, Australia. METHODS: We used workers' compensation claims identified as COVID-19 infection related from 1 January 2020 to 31 July 2022 to compare COVID-19 infection claims and rates of claims by industry and occupation, and in relation to Victorian COVID-19 epidemiology. A Cox proportional hazards model assessed risk factors for extended claim duration. RESULTS: Of the 3313 direct and indirect COVID-19-related claims identified, 1492 (45.0%) were classified as direct COVID-19 infection accepted time-loss claims and were included in analyses. More than half (52.9%) of COVID-19 infection claims were made by healthcare and social assistance industry workers, with claims for this group peaking in July-October 2020. The overall rate of claims was greater in the healthcare and social assistance industry compared with all other industries (16.9 vs 2.4 per 10 000 employed persons) but industry-specific rates were highest in public administration and safety (23.0 per 10 000 employed persons). Workers in healthcare and social assistance were at increased risk of longer incapacity duration (median 26 days, IQR 16-61 days) than in other industries (median 17 days, IQR 11-39.5 days). CONCLUSIONS: COVID-19 infection claims differed by industry, occupational group, severity and timing and changes coincided with different stages of the COVID-19 pandemic. Occupational surveillance for COVID-19 cases is important and monitoring of worker's compensation claims and incapacity duration can contribute to understanding the impacts of COVID-19 on work absence.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".