Creating a compelling business case for employers; psychosocial benefit for mature workers
Bibliographic record
Abstract
Twenty years ago, those of us in the allied health field were trained to believe that mature people didn't lodge workers’ compensation claims and mature workers didn't have injuries because they ‘knew their work environment so well’. Only 7 years ago, insurers believed that their book of claims in Workers’ Compensation had no age related correlation to claims costs. We now know that all of these assumptions do not stand up in relation to the current workforce in Australia and we are building a wealth of knowledge about the mature cohort in today's workforce. This presentation will guide the audience through the process of developing a compelling business case to assist employers understand the cost benefit of proactively engaging with their mature employees and the benefits for both the workers over fifty five years of age and businesses in Australia. In developing a business case, the first stage of a strategy is being formulated for the care and well-being of mature workers, this leads to preparedness for effective return to work measures in the event of injury and health maintenance strategies for mature workers. The side benefits to a business are vast; from reduced turnover and associated costs, the retention of knowledge to reduced insurance costs and productivity benefits. Five years of analytical data will be presented to demonstrate the trending, the influences and precautionary tales for those committed to the psychological well-being of the mature workforce in Australia.
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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".