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Functional interdependence

2006· article· en· W1969162526 on OpenAlexaff
Mark Del Aguila, Lisa Sanderson Cox, Louisa Lee

Bibliographic record

VenueAustralasian Journal on Ageing · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsSimon Fraser University
FundersAustralian Rotary HealthRotary Foundation
KeywordsResidenceContext (archaeology)Service (business)GerontologyDiscriminant function analysisPsychologyIsolation (microbiology)MedicineBusinessGeographyDemographySociologyMarketingComputer science

Abstract

fetched live from OpenAlex

Objective: The interrelationship between functional capacity, informal networks and the physical environment of the residence and residential location is used to describe age‐care service utilisation and non‐utilisation. Methods: Fifty‐two applicants for home‐care services were matched with 52 non‐applicants, and 40 applicants for day‐care services were matched with 40 non‐applicants according to age, gender, mental status, and physical functioning. Results: Discriminant Function Analyses indicated home‐care applications are related to network isolation within existing neighbourhoods and that day‐care applicant networks were insufficient to accommodate challenges presented by the immediate physical environment of the residence. The physical environment of the residence also distinguished home‐care applicants from day‐care applicants. Conclusion: The findings support the proposed model of functional interdependence that describes service utilisation and non‐utilisation as a function of the interrelationship between functional capacity and the capacity of family, friends, neighbours and communities of interest to accommodate challenges present in the elder persons residence and residential context.

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)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.032
GPT teacher head0.320
Teacher spread0.288 · 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 designNot applicable
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

Citations12
Published2006
Admission routes1
Has abstractyes

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