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Record W2387947035 · doi:10.1111/1753-6405.12525

Comprehensive clinical assessment of home‐based older persons within New Zealand: an epidemiological profile of a national cross‐section

2016· article· en· W2387947035 on OpenAlexaff
Philip J. Schlüter, Annabel Ahuriri‐Driscoll, Tim Anderson, Paul Beere, Jennifer Brown, John C. Dalrymple‐Alford, T J David, Andrea Davidson, Deborah Gillon, John P. Hirdes, Sally Keeling, Simon Kingham, Cameron Lacey, Andrea Menclova, Nigel Millar, Vince Mor, Hamish A. Jamieson

Bibliographic record

VenueAustralian and New Zealand Journal of Public Health · 2016
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Waterloo
FundersNew Zealand Government
KeywordsEpidemiologySection (typography)MedicineGerontologyEnvironmental healthCross-sectional studyFamily medicineGeographyPathologyBusinessAdvertising

Abstract

fetched live from OpenAlex

OBJECTIVE: Since 2012, all community care recipients in New Zealand have undergone a standardised needs assessment using the Home Care International Residential Assessment Instrument (interRAI-HC). This study describes the national interRAI-HC population, assesses its data quality and evaluates its ability to be matched. METHODS: The interRAI-HC instrument elicits information on 236 questions over 20 domains; conducted by 1,800+ trained health professionals. Assessments between 1 July 2012 and 30 June 2014 are reported here. Stratified by age, demographic characteristics were compared to 2013 Census estimates and selected health profiles described. Deterministic matching to the Ministry of Health's mortality database was undertaken. RESULTS: Overall, 51,232 interRAI-HC assessments were conducted, with 47,714 (93.1%) research consent from 47,236 unique individuals; including 2,675 Māori and 1,609 Pacific people. Apart from height and weight, data validity and reliability were high. A 99.8% match to mortality data was achieved. CONCLUSIONS: The interRAI-HC research database is large and ethnically diverse, with high consent rates. Its generally good psychometric properties and ability to be matched enhances its research utility. IMPLICATIONS: This national database provides a remarkable opportunity for researchers to better understand older persons' health and health care, so as to better sustain older people in their own homes.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.287
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.297
GPT teacher head0.519
Teacher spread0.222 · 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 teacher head, 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

Citations73
Published2016
Admission routes1
Has abstractyes

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