Development and validation of disability management indicators for the construction industry
Bibliographic record
Abstract
Purpose Support at the organizational and managerial levels defines the degree to which construction workplaces can accommodate disabled and injured workers. There is little empirical evidence about the indicators and practices that can be used by construction organizations to evaluate disability management (DM). This paper aims to develop and validate key indicators and practices of disability/injury management within construction. Design/methodology/approach To achieve this, the research used a two-phase sequential exploratory review of literature, followed by a quantitative phase, using analytic hierarchy process. The analytical hierarchy process (AHP) involved recruiting eight health and safety and DM experts to conduct pairwise comparisons of these indicators. Findings The results found return-to-work and disability and injury management practices to be the most important indicators and physical accessibility and claims management practices to be the least important. Practical implications The development of these indicators should help construction organizations develop DM programs that better meet their needs, and benchmark and improve related performance. Social implications The results could also be useful for all stakeholders in general and decision makers in particular involved within construction. Originality/value Such prioritization helps organizations to prioritize their DM practices thereby optimizing performance.
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 | 0.063 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".