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Record W4223894733 · doi:10.1177/08982643221085107

Wellness in the Face of Frailty Among Older Adults in First Nations Communities

2022· article· en· W4223894733 on OpenAlexafffundabout
Morgan Slater, Gabrielle Bruser, Roseanne Sutherland, Melissa K. Andrew, Wayne Warry, Kristen Jacklin, Jennifer Walker

Bibliographic record

VenueJournal of Aging and Health · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsDalhousie UniversityMcMaster UniversityLaurentian UniversityImpactQueen's University
FundersInstitute of Aboriginal Peoples HealthCanadian Institutes of Health Research
KeywordsGerontologyLogistic regressionMental healthBalance (ability)Multivariate analysisPsychologySuccessful agingMultivariate statisticsMedicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: First Nations people report high levels of wellness despite high rates of chronic illness. Our goal was to understand the factors associated with wellness among First Nations adults in Ontario who were considered frail. METHODS: Using the First Nations Regional Health Survey, we created a profile of First Nations adults (aged 45+) who were categorized as "frail" (weighted sample size = 8121). We used multivariate logistic regression to determine associations between wellness (as measured by self-reported physical, emotional, mental, and spiritual balance) and determinants of health. RESULTS: Rates of reported wellness were high among those who were frail, ranging from 56.7% reporting physical balance to 71.6% reporting mental balance. Three key elements were associated with wellness: the availability of resources, individual lifestyle factors, and cultural connection and identity. DISCUSSION: Our findings provide a profile of strength and wellness among older First Nations adults living with frailty.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
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 head0.032
GPT teacher head0.341
Teacher spread0.309 · 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.

Study designQualitative
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

Citations2
Published2022
Admission routes3
Has abstractyes

Explore more

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