Comparative analysis of leading and lagging indicators of construction disability management performance: an exploratory study
Bibliographic record
Abstract
To enable organisations holistically manage work related injuries, the Construction Disability Management Maturity Model (CDM3) and disability management (DM) metrics were developed. The model benchmarks DM and provides strategies to optimize performance. The model was validated on a sample of construction organisations in Manitoba. 12 disability management metrics were developed and used to measure the lagging, or after-the-fact performance, of the organisations. The paper explores the relationship between the leading and lagging indicators of DM performance. Analysis showed that companies with higher maturity scores had relatively lower rates of return, and companies with lower maturity scores had higher rates of return, comparatively. The findings also showed that companies with higher DM maturity had lower recordable injury rates, severity rates and lost time case rates than companies with lower DM maturity. To this end, a more complete implementation of the CDM3 and the developed metrics is the main recommendation of the study. The main implication of the findings of the study is that disability management performance improvement strategies must consider both leading and lagging indicators of performance to bring about sustainable improvements in the construction industry.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".