Comparative analysis of leading and lagging indicators of construction disability management performance: an exploratory study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it