Stroke Rehab Down Under: Can Rupert Murdoch, Crocodile Dundee, and an Aboriginal Elder Expect the Same Services and Care?
Bibliographic record
Abstract
Australia is the world’s sixth largest country, has a relatively small population of 21.5 million, and a blended (public and private) health system. In this article, we explain the stroke rehabilitation infrastructure including consumer organisations, research networks, data collection systems, and registries. This represents a complex but fledgling set of organisations showing great promise for coordination of care and research. The article goes on to expose the inequalities in service provision by describing the paths of stroke survivors in three settings – in the city, in the country, and in remote settings. The complexities and difficulties in treating indigenous stroke survivors are described in a culturally sensitive narrative. The article then discusses the outcomes of the first Australian audit of post acute stroke services completed in December 2008, which describes the journeys of 2,119 stroke survivors at 68 rehabilitation units throughout Australia’s 6 states and 2 territories. It demonstrates an average length of stay of 26 days, with 18% of survivors requiring nursing home or other supported accommodation. The article concludes with future directions for stroke rehabilitation in Australia, which include hyperacute rehabilitation trials, studies in 7-days-a-week rehabilitation, and the potential use of robotics.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.022 | 0.016 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".