Disability management in a sample of Australian self-insured companies
Bibliographic record
Abstract
PURPOSE: Disability management (DM) is a term developed in North America and refers to the prevention and management of injury and illness in the workplace. The purpose of this paper is to report findings of an Australian study that examined whether self-insured employers in that country have implemented integrated DM programmes. Key principles underpinning such programmes are explored to identify the extent to which Australian employers have adopted them. METHOD: Data was collected from 29 self-insured Australian companies in three Australian States using a structured interview format with additional open-ended questions. RESULTS: It was found that companies have in place, to varying degrees, some of the key elements of disability management programmes. However, these elements were often not well integrated in a comprehensive disability management approach. The focus on workplace-based, early intervention in the area of return to work for injured employees was particularly strong, but there was little evidence of formal labour-management committee structures responsible for implementing DM programmes. CONCLUSIONS: If the concept of DM is relevant to the Australian environment then this study would suggest that self-insured companies need to undertake further work to develop integrated approaches to preventing and managing disability in the workplace. Several limitations of this study are highlighted and it is concluded that further work in this area is needed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".