impact of workers’ compensation benefit cessation on welfare and health service use
Bibliographic record
Abstract
BACKGROUND: In 2012, the Australian state of New South Wales passed legislation that reformed its workers' compensation system. Section 39 introduced a five-year limit on income replacement, with the first affected group having their benefits cease in December 2017. There is limited evidence on how this will affect their healthcare service use and where they will go for financial support. METHODS: Multiple data sources will be linked: administrate workers' compensation claims data from the State Insurance Regulatory Authority (SIRA), universal health insurance data from the Medical Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS), state hospital and emergency department data, and social welfare data from the Department of Social Services' Data Over Multiple Individual Occurrences (DOMINO). An estimated 4,125 injured workers had their benefits cease due to Section 39. These will form the exposure group who will be compared to 1) a similar group of workers' compensation claimants who have had at least two years of compensated time off work but whose benefits did not cease due to Section 39; and 2) a community comparison group drawn from state hospital and emergency department records.An accredited third party will link the data, which will be accessible only via secure virtual machine. Initial analyses will compare the prevalence and incidence of service use across groups in both the year before and year after benefit cessation; the community control will be assigned the median benefit cessation date in lieu of an actual date. To estimate the impact of benefit cessation due to Section 39, we will conduct time series analysis of the prevalence and incidence of service use. DISCUSSION: This study will provide much-needed evidence on the consequences of long-term benefit cessation, particularly on subsequent healthcare and welfare service use.
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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 | 0.003 | 0.008 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".