Disability Management Best Practices and Joint Labour–Management Collaboration
Bibliographic record
Abstract
Abstract The purpose of this article is to discuss the rationale for labour involvement in the management of injury and disability at the workplace. The authors discuss the importance of joint labour–management collaboration in the development and implementation of disability management and return-to-work programs. Best practices in disability management are reviewed, with a focus on important implications for joint labour–management committees. An overview of the key elements of formal return-to-work programs is discussed, including established benchmarks for return-to-work program development and implementation. This article also provides an overview of a proposed pioneering research project to analyse collective bargaining agreements, to review evidence of a joint commitment to best practices in worksite disability management, and to determine the extent to which disability management best practices are evident within unionised worksites.
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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 | 0.073 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".