MétaCan
Menu
Back to cohort
Record W4214938073 · doi:10.1177/07334648211065431

Perceived Community Age-friendliness is Associated With Quality of Life Among Older Adults

2022· article· en· W4214938073 on OpenAlexafffund
Nadia Mullen, Arne Stinchcombe, Charles Seguin, Shawn Marshall, Gary Naglie, Mark Rapoport, Holly Tuokko, Michel Bédard

Bibliographic record

VenueJournal of Applied Gerontology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsNOSM UniversityUniversity of VictoriaHealth Sciences CentreSunnybrook Health Science CentreUniversity of TorontoSt. Joseph's Care GroupOttawa HospitalBruyèreBaycrest HospitalUniversity of OttawaLakehead University
FundersCanadian Institutes of Health Research
KeywordsGerontologyQuality of life (healthcare)Psychological interventionMultilevel modelPsychologyScale (ratio)Explained variationDemographyVariance (accounting)Regression analysisMedicineGeography

Abstract

fetched live from OpenAlex

We examined the positive association between perceived community age-friendliness and self-reported quality of life for older adults. A total of 171 participants, aged 77-96 years, completed a mail-in questionnaire package that included measures of health (SF-36 Physical), social participation (Social Participation Scale), community age-friendliness (Age-Friendly Survey [AFS]), and quality of life (WHO Quality of Life). Hierarchical regression models including age, gender, driving status, finances, health, social participation, and AFS scores explained 8 to 21 per cent of the variance in quality of life scores. Community age-friendliness was a statistically significant variable in all models, accounting for three to six and a half per cent of additional variance in quality of life scores. Although the proportion of variance explained by age-friendliness was small, our findings suggest that it is worthwhile to further investigate whether focused, age-friendly policies, interventions, and communities could play a role towards successful and healthy aging.

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)0.003
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 score0.380
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.353
Teacher spread0.293 · 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

Citations17
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied GerontologySame topicHealth disparities and outcomesFrench-language works237,207