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Record W7117715352 · doi:10.3390/jal6010004

Examining Psychosocial Factors Influencing Nutrition Risk in Middle-Aged and Older Adults: Findings from the Canadian Longitudinal Study on Aging

2025· article· en· W7117715352 on OpenAlexaffabout
Christine Marie Mills, Catherine Donnelly

Bibliographic record

VenueJournal of Ageing and Longevity · 2025
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsQueen's UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsPsychosocialLongitudinal studyLogistic regressionCohort studyRisk factorCohortLongitudinal dataTracking (education)

Abstract

fetched live from OpenAlex

Nutrition risk is prevalent in community-dwelling older adults, and leads to increased morbidity and mortality. Understanding the factors associated with the development of high nutrition risk is crucial for the development of appropriate programs and policies to address this problem. Therefore, our objective was to identify the psychosocial factors correlated with the development of high nutrition risk, as assessed by SCREEN-8, among Canadian adults categorized by ten-year age groups (45–54, 55–64, 65–74, and 75+). We used data from 17,051 participants in the tracking cohort of the Canadian Longitudinal Study on Aging and employed multivariable binomial logistic regression to identify the social and demographic factors associated with the emergence of high nutrition risk at follow-up, three years after the baseline. Baseline data were gathered between 2011 and 2015. At baseline, 34.4% of participants across all age groups were at high nutrition risk, while 40.0% were at high risk at follow-up. Factors consistently associated with the development of high nutrition risk across all age groups included lower levels of social support, lower self-rated social standing, infrequent participation in sports or physical activities, infrequent participation in cultural or educational activities, and lower household incomes. Programs and policies addressing these factors may reduce the prevalence of high nutrition risk and the development of high nutrition risk.

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 score0.054
Threshold uncertainty score0.964

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 integrity0.0000.001
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 head0.068
GPT teacher head0.339
Teacher spread0.271 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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