Australia Long-term Care Highlights from Help Wanted? Providing and Paying for Long-
Bibliographic record
Abstract
� Australia expenditure on long-term care as a share of GDP is expected to at least double, and could even triple, by 2050. � The demand for LTC workers as a share of the working population is set to increase by 140%, over the same period. � Carers in Australia are 30 % more likely to hold a temporary job and have nearly three years shorter working career than non-carers. Measures such as flexible working time can help work-care reconciliation. As in the case of the United Kingdom, the use of flexible working time is found to increase carers ’ working hours in Australia. On the other hand, cash benefits to the carer can raise policy trade-offs. While Australia is one of the few countries with a carer allowance, means-tested allowances generate incentives for carers to reduce hours of work (as in the United Kingdom). Low-skilled women are especially at risk. � Working-age carers are at a higher risk of poverty. Caregiving is associated with a higher probability of experiencing poverty in Australia as in most other OECD countries, except in southern Europe. Women carer appear to be especially vulnerable to poverty risks. � Australia, the United Kingdom and Canada are among the few countries with immigration programmes that can apply to long-term care workers. Good initiatives to attract and retain care workers should be continued, such
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".