It is time for nationally equitable access to assistive technology and home modifications in Australia: An equity benchmarking study
Bibliographic record
Abstract
Abstract Australians with disability have inequitable access to assistive technology (AT) and home modifications (HMs). This is inconsistent with human rights obligations and fails to capitalise on internationally recognised potential return on investment. Co‐designed with a consortium of AT stakeholders, this study quantifies the public provision of AT and HM in Australia by identifying all publicly funded national and state‐/territory‐based schemes and reporting and comparing available data on the spend per person. An environmental scan and data survey identified 88 government funders administering 109 schemes. Data were available for 1/3 of schemes. Economic evaluation of available cost and participant data estimated the annual AT/HM and wrap‐around support spend per person per scheme and organisational costs. Data demonstrated significant AT/HM spend variability across schemes, for example a 50‐fold difference between Aged Care ($51) and National Disability Insurance Scheme (NDIS, $2500). Modelled costs are presented for a $16 billion national scheme where all Australians with disability are funded NDIS‐equivalent. These foundation data demonstrate substantial service provision gaps and an urgent need for change in disability policy. A cost model and policy principles have been proposed to achieve economies of scale and equity in the provision of AT and HM.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".