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Record W2116449813 · doi:10.3138/cja.27.1.069

Can Canadian Seniors on Public Pensions Afford a Nutritious Diet?

2008· article· en· W2116449813 on OpenAlexaffabout
Patricia L. Williams, Shanthi Johnson, Ilya Blum

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2008
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsSaskatchewan HealthDalhousie UniversityUniversity of ReginaMount Saint Vincent University
Fundersnot available
KeywordsBusinessAgricultural economicsGerontologyMedicineEconomics

Abstract

fetched live from OpenAlex

This study examined whether Canada's public pensions (Old Age Security and Canada Pension Plan) provided adequate income for seniors living in Nova Scotia in 2005 to afford a basic nutritious diet. Monthly incomes were compared to essential monthly expenses for four household scenarios: (a) married couple, 80 years and 78 years, in urban Nova Scotia; (b) single male, 77 years in rural Nova Scotia; (c) a couple, 70 years and 65 years, in rural Nova Scotia; (d) widowed female, 85 years, in urban Nova Scotia. The monthly food costs for the four households were CAN$313.32, $193.83, $316.71, and $150.89, respectively. Results showed that both single-member households lacked the necessary funds for a nutritious diet, while living with a partner seemed to protect against inadequate financial resources. These findings illustrate the need to improve Canada's retirement systems to ensure all seniors have adequate financial resources to meet their basic needs-including nutritious food-prevent chronic disease, and ultimately improve quality of life.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.981
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.084
GPT teacher head0.324
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), 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
Published2008
Admission routes2
Has abstractyes

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