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Record W2940899449 · doi:10.1177/0008417419838360

“We need more things for us”: Being low income and underoccupied in older age

2019· article· en· W2940899449 on OpenAlexvenueno aff
Kendra S. Heatwole Shank, Benjamin Kenley, Stacy Brown, Jenna Shipley, Maya Baum, Chloe Beers

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2019
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGerontologyPsychologySociologyMedicine

Abstract

fetched live from OpenAlex

BACKGROUND.: Low-income older adult populations have increased vulnerability to occupational engagement barriers and poor health outcomes while aging in community settings. PURPOSE.: The purpose of this study was to examine the relationship between community navigation and well-being for low-income older adults. METHOD.: = 10) were recruited for this multimethod observational cohort study, which employed GPS data, measures of well-being, and semistructured interviews across 12 months. Grounded theory processes were followed to analyze and integrate the qualitative, quantitative, and spatial data. FINDINGS.: Findings were three patterns of community navigation. In particular, patterns of being chronically underoccupied were noted for this low-income population. Specific place-based challenges are explained along with strategies used to mitigate these challenges. IMPLICATIONS.: Supporting community navigation, especially social interaction opportunities, can maximize well-being; and older residents' occupational participation may be unnecessarily curtailed by geographic, economic, and social factors beyond their control. Community navigation strategies should be considered holistically by occupational therapists as part of interventions supporting aging in place.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.997

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 head0.089
GPT teacher head0.420
Teacher spread0.331 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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