Evaluating disability management in the Manitoban construction industry for injured workers returning to the workplace with a disability
Bibliographic record
Abstract
The poor safety record of the construction industry raises concerns about the extent to which it is able to integrate workers disabled as a result of a workplace injury back to the workplace. A review of the literature indicates there is little empirical evidence about the status of disability management (DM) in the Canadian construction industry, specifically with respect to injured construction workers returning to the workplace with a disability. To address this limitation, a web-based survey was administered to a sample of Manitoban construction organizations to enquire about workers disabled as a result of a workplace injury in the industry, practices in place to accommodate them, and barriers to their employment. The analysis of the responses of 88 organizations showed that the majority of responding organizations employed few disabled workers. Disabilities due to musculoskeletal injuries were the most common, followed by physical mobility and hearing impairments. Respondents saw retaining valued and experienced employees and maintaining employee morale as the main reasons for implementing a DM program. They also found the lack of suitable modified or alternate work to be the most important barrier to DM; however, they identified the provision of such work as the most common practice implemented by them, raising questions about this work’s suitability to disabled workers.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".