The National Disability Insurance Scheme and <scp>COVID</scp> ‐19: a collision course
Bibliographic record
Abstract
To the Editor: The National Disability Insurance Scheme (NDIS) is one of the largest health reforms in Australia's history.1 The scheme aims to give people with a disability choice and control over their daily lives.2 It is designed to operate as nation-wide disability “markets” from which services can be “purchased”.2 NDIS participants are allocated a budget from which they purchase the services they require. The NDIS is very different from our previous disability models, which saw people receiving standardised services from a more limited number of government and not-for-profit organisations, and a less decentralised workforce. The NDIS is a visionary reform; however, we are now seeing that it is also designed to spread an epidemic such as coronavirus disease 2019 (COVID-19) to thousands of people with a disability. The NDIS has created a “gig economy” within the disability services sector. Individuals are paid for discrete services, from showering and feeding, to social support activities, to household tasks. This means as many as ten different carers entering a participant's home, performing a care service, and then moving on to another home. The workforce is now predominantly casual, and there are growing numbers of self-employed.3 This structure is primed to spread infection because: Unfortunately, many people who are part of the NDIS have comorbidities,4 making them vulnerable to COVID-19 by both physiology and system design. Previous research has raised concerns about the readiness of the workforce to handle complex disability under normal circumstances, let alone in the context of a pandemic.5 While government agencies are working to communicate hygiene practices with NDIS participants, challenges such as personal protective equipment shortages and high worker motility need to be addressed. Otherwise, the health care system will need to ready itself for a disproportionate number of people with disability. I am an investigator at the NHMRC Centre of Research Excellence in Disability and Health. No relevant disclosures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".