Understanding changes over time in workers' compensation claim rates using time series analytical techniques
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to better understand the inter-temporal variation in workers' compensation claim rates using time series analytical techniques not commonly used in the occupational health and safety literature. We focus specifically on the role of unemployment rates in explaining claim rate variations. METHODS: The major components of workers' compensation claim rates are decomposed using data from a Canadian workers' compensation authority for the period 1991-2007. Several techniques are used to undertake the decomposition and assess key factors driving rates: (i) the multitaper spectral estimator, (ii) the harmonic F test, (iii) the Kalman smoother and (iv) ordinary least squares. RESULTS: The largest component of the periodic behaviour in workers' compensation claim rates is seasonal variation. Business cycle fluctuations in workers' compensation claim rates move inversely to unemployment rates. CONCLUSIONS: The analysis suggests that workers' compensation claim rates between 1991 and 2008 were driven by (in order of magnitude) a strong negative long term growth trend, periodic seasonal trends and business cycle fluctuations proxied by the Ontario unemployment rate.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".