Predicting short term disability benefit days for the Workers’ Compensation Board of British Columbia
Bibliographic record
Abstract
The objective of this study was to predict Short Term Disability (STD) benefits paid by the Workers' Compensation Board of British Columbia. An STD benefit is defined as the total number of wage days that a worker is unable to work because of an injury incurred at the workplace. Factors which may explain variability in STD days paid at the claim level were studied with regression analysis and ranked according to the level of importance. This analysis indicated that injury related factors have more of an effect on STD days than other factors such as age and occupation. Regression analysis was used to study total STD days paid within the truncation periods and to predict the total STD days in the coming 6, 12, and 18 months at the area office and WCB levels. The model developed to total STD days paid within the truncation periods showed the relative difference of STD days within each factor. For example, this model showed that the older the claimant is, the more STD days they are likely to receive. The model to predict the total STD days in the coming 6, 12, and 18 months at the area office and WCB level yielded accurate results as measured by a comparison of the estimated STD days and the actual STD days paid in the past. Survival analysis was used to estimate additional and total STD days given that a claim has already received more than a certain number of STD days. The model showed that the more STD days a claimant has received so far, the more additional and total STD days the claimant will receive. Knowing the influential factors on STD days will help WCB seek possible methods to reduce STD days. The predictive models for the coming 6, 12, and 18 months will help the process of budgeting. Finally, based on the predictive model of the additional and total STD days, WCB can anticipate how many STD days will be paid on a particular type of claim given that a claim has already received more than a certain number of days of payment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".