Evaluating disability management in construction using maturity modelling and metrics and its relation to safety performance
Bibliographic record
Abstract
Despite the potential benefits of disability management (DM) programs with respect to reducing costs and improving workplace morale, many organizations in the construction industry appear unable to develop and implement them. Inadequate support and practices at the organizational level affect the degree to which construction workplaces can accommodate disabled employees returning to the workplace with a disability, reinforcing the need to investigate the maturity of related practices. This research aimed to investigate disability management in the Manitoban construction industry and its relation to safety performance. The research developed the Construction Disability Management Maturity Model, which benchmarks construction companies’ disability management performance using 12 indicators. The weights of importance of these indicators was determined by eight experts using an analytical hierarchy process. The model was then applied to a sample of 10 general contractors in Manitoba using an assessment worksheet. The research also involved adopting three safety metrics and developing 12 new DM metrics and using them to evaluate the safety and DM performance of the same general contracting companies. The maturity model results were correlated to the disability management and safety performance metric results to investigate the relationship between construction organizations’ DM maturity and their DM and safety performance. The analytical hierarchy process showed “Return to work and accommodation” and “Disability and injury management” practices were the most critical indicators whereas “Physical accessibility management” and “Claims management” practices were the least critical. The results also showed after applying the CDM3 that the ten construction companies operated at the quantitatively managed level. “Senior management support” and “Disability and injury prevention” were found to be the most mature indicators whereas “Retention and recruitment” and “Communication” practices were the least mature. The findings also showed that companies with higher DM maturity tended to record lower recordable injury rates, lower severity rates and lower lost time case rates, and thus have higher safety performance than companies with lower DM maturity. This research is the first ever to deliver leading and lagging indicators of performance in the form of the CDM3 and metrics that organizations can use to evaluate and benchmark their DM performance.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".