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Record W3198519363 · doi:10.1002/ajs4.184

Young people in residential aged care: Is Australia on track to meet its targets?

2021· article· en· W3198519363 on OpenAlexaboutno aff
Mark Brown, Amelia Condi, Elise Davis, Isabella Goodwin, Di Winkler, Jacinta Douglas

Bibliographic record

VenueAustralian Journal of Social Issues · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Aged careProject commissioningResidential carePublishingGovernment (linguistics)GerontologyOlder peopleEconomic growthDemographic economicsBusinessPolitical scienceMedicineGeographyEconomics

Abstract

fetched live from OpenAlex

Abstract Over 4500 people under 65 years of age live in residential aged care (RAC) in Australia, and they experience poorer quality of life than people with similar disabilities in other settings. Governments have long aimed to reduce admissions of young people to RAC, but in 2019, for the first time, the Australian Government adopted target dates for resolving the issue. The targets include reducing admissions of young people to near zero by 2022 and ensuring almost no one remains in RAC beyond 2025. The national strategy focuses mostly on housing and support needs being met via the National Disability Insurance Scheme. The present study drew on quarterly data from the National Aged Care Data Clearinghouse to examine progress toward these targets. Significant progress was evident in terms of young people entering RAC: admissions reduced each quarter between September 2018 and July 2020, halving over two years. No progress was evident in terms of young people leaving RAC for better arrangements; the trend neither increased nor decreased. Prospects for achieving the targets are discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.347

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.099
GPT teacher head0.435
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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