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Record W2154265608 · doi:10.1177/154193120905300808

Understanding Aging in Place for Older Adults: A Needs Analysis

2009· article· en· W2154265608 on OpenAlexaff
Cara Bailey Fausset, Andrew K. Mayer, Wendy A. Rogers, Arthur D. Fisk

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2009
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of Calgary
FundersNational Institute on AgingNational Institutes of Health
KeywordsPsychological interventionGerontologyMetropolitan areaPsychologyAtlantaSuccessful agingAging in placeMedicinePsychiatry

Abstract

fetched live from OpenAlex

A goal of many older adults is to remain in their own homes as they age (Beyond 50.05 Survey, 2005). However, a detailed assessment is lacking of the needs of older adults as they age in place. Using focus groups, twenty-six independently living older adults (mean age 78.8 years) from the Atlanta metropolitan area were asked to describe the tasks they perform to maintain their homes, as well as any difficulties they have performing these tasks. Participants described performing a wide range of tasks and focused primarily on physical difficulties. However, participants also reported solutions to manage these difficulties that fell into three broad categories: "Cessation," "Perseverance," and "Compensation." These categories represent classes of opportunities for interventions that may help older adults remain independent in their homes longer. By understanding the nature of home maintenance problems older adults encounter while aging in place, interventions and redesign efforts can be more effective. These data suggest that interventions should start with answering physical issues.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.050
GPT teacher head0.315
Teacher spread0.266 · 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

Citations16
Published2009
Admission routes1
Has abstractyes

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