Construction disability management maturity model: case study within the Manitoban construction industry
Bibliographic record
Abstract
Purpose A review of the literature revealed a lack of coherent frameworks for implementing disability management, particularly within the construction industry. This study involved developing the construction disability management maturity model (CDM3) to assess the maturity of disability management (DM) practices in construction organisations. Design/methodology/approach In its current form, the model assessed twelve indicators using a series of questions representing relevant best practices for each indicator and five different maturity levels. An analytical hierarchical process was conducted using eight construction and DM experts to determine the weights of importance of these different indicators. The model was then applied to evaluate ten construction companies in Manitoba, Canada. Findings The results revealed that the indicators of “Return to Work”, “Disability and Injury Prevention”, and “Senior Management Support” practises were the most heavily weighted and, thus, the most important. Companies' DM performance was observed, on average, to be at the quantitatively managed level. “Senior Management Support” and “Disability Injury Prevention” practices were observed to be the most mature indicators on average, revealing a potential relationship between the most important and most mature indicators. Research limitations/implications The sample size of companies evaluated is a key limitation in that it does not permit for the generalisation of the results. Practical implications This study provided a framework for benchmarking the DM performance of construction organisations. Originality/value No similar maturity model has been developed to date to assess DM in construction, making the CDM3 the first of its kind to evaluate a construction organisation's existing DM practices against best practises.
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".