A comparison of disability management practices in Australian and Canadian workplaces
Bibliographic record
Abstract
The health, well-being and productivity of workers and employers in today's society is becoming increasingly important. The social, emotional and economic costs of injury and illness are such that governments throughout the world are attempting to implement policies and practices to contain these costs. One response in this area is Disability Management (DM). DM focuses on the management of employees with work injuries or illnesses in the workplace rather than offsite in rehabilitation centres. Regional interest in the DM approach has now gained momentum in North America, Europe and the Asia-Pacific. This article briefly reviews two studies that were conducted in Australia and Canada (results have or are being published elsewhere). Although the two studies were not designed for comparison purposes they provide interesting and useful information about the similarities and differences in the practice of DM in Australia and Canada. Findings are compared in terms of five primary principles of DM and it is argued that it is important to understand the ecological contexts in which DM occurs as well as share trans-national research in this area to help inform policy and practice.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.007 | 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".