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Record W2042787679 · doi:10.1177/0733464809345495

Would Older Adults Turn to Community Support Services for Help to Maintain Their Independence?

2009· article· en· W2042787679 on OpenAlexafffund
Margaret Denton, Jenny Ploeg, Joseph Tindale, Brian Hutchison, Kevin Brazil, Noori Akhtar‐Danesh, Jean Lillie, Jennifer Plenderleith

Bibliographic record

VenueJournal of Applied Gerontology · 2009
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of GuelphMcMaster University
FundersUniversity of Guelph
KeywordsVignetteGerontologyIndependence (probability theory)Activities of daily livingAging in placePsychologyMedicineSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

The purpose of this article is to determine whether middle-aged and older adults would identify community support services (CSSs) as a source of assistance for difficulties with the instrumental activities of daily living (IADLs). Furthermore, we determine factors related to the identification of home health and CSSs.Telephone interviews were conducted with 768 adults aged 50 and older. Respondents were presented with a vignette describing a situation where loss of independence is threatened. They were asked what they would do in that situation. Although less than 20% mentioned CSSs, nearly 50% mentioned either a home health or CSS. Findings suggest those less likely to mention a home health or CSS include men, older adults, and the foreign born. In addition, those with less education, functional health limitations, no social support, and a lack of knowledge of where to find information about CSSs mentioned home health or CSSs less often.

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)0.009
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.039
GPT teacher head0.382
Teacher spread0.343 · 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

Citations12
Published2009
Admission routes2
Has abstractyes

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