O-352 Evaluating program effectiveness on return-to-work after work-related injury in the construction sector
Bibliographic record
Abstract
Introduction Return-to-work (RTW) in the construction sector is more challenging than in many other sectors. Between 2010 and 2011, the Ontario Workplace Safety Insurable Board (WSIB) introduced the Work Reintegration (WR) program in an attempt to improve RTW outcomes for injured workers. Objective To determine whether the WR program was associated with reducing work disability duration in the Ontario construction sector. Methods WSIB claims data were extracted for construction workers compensated for time off work following work-related injuries betwen the years 2009 and 2015 (n=27,131). Claims receiving referrals to return-to-work (RTW) and vocational rehabilitation (VR) specialists were propensity score matched with claims receiving no referrals. Multivariable quantile regression models were used to examine differences in the cumulative disability days paid during two-years post-injury between the groups of claims before and fater the WR intervention period and the differences in these differences. Results Prior to the WR program, cumulative disability days paid was greatest among claims referred to VR specialists, followed by claims referred to RTW specialists (390 and 109 additional days than claims with no referrals, respectively). Following the WR program intervention, cumulative disability days paid reduced for all claims but most notably among longer duration claims referred to RTW specialists (reduction of 274 days at the 90th percentile in the disability distribution) and shorter duration claims referred to VR specialists (reductions of 255 and 214 days at the 25th and 50th percentiles in the disability distribution, respectively). Conclusion The WR program intervention was effective in reducing the cumulative disability days paid for construction worker claims targeted under the various referrals. While the effects varied at different percentiles in the disability distribution, and by specialist referral, future research should examine the type and timing of services received to more fully understand what may be driving the overall findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".