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Record W2015206712 · doi:10.1080/21551197.2013.781419

Exploration of Functional Food Consumption in Older Adults in Relation to Food Matrices, Bioactive Ingredients, and Health

2013· article· en· W2015206712 on OpenAlexaff
Meagan N. Vella, Laura M. Stratton, Judy Sheeshka, Alison M. Duncan

Bibliographic record

VenueJournal of Nutrition in Gerontology and Geriatrics · 2013
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFunctional foodMedicineConsumption (sociology)Environmental healthFood scienceHealth benefitsDietary fiberGerontologyTraditional medicine

Abstract

fetched live from OpenAlex

The functional food industry is expanding, yet research into consumer perceptions of functional foods is limited. Older adults could benefit from functional foods due to age-related food and health issues. This research gathered information about functional foods from community-dwelling older adults (n = 200) who completed a researcher-administered questionnaire about consumption, food matrices, bioactive ingredients, and health areas addressed through functional foods. Overall prevalence of functional food consumption was found to be 93.0%. Commonly consumed foods included yogurt with probiotics (56.0%), eggs with omega-3 fatty acids (37.0%), and bread with fiber (35.5%). Functional food matrices primarily consumed were yogurt (51.5%), bread (44.0%), and cereal (40.0%). The primary functional food bioactive consumed was dietary fiber (79.5%). Most participants (86.2%) indicated that they consume functional foods to improve health, and the major areas specified were osteoporosis/bone health (67.5%), heart disease (61.0%), and arthritis (55.0%). These results inform health professionals regarding the potential of functional foods to support health among older adults.

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)0.000
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 score0.021
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.060
GPT teacher head0.306
Teacher spread0.246 · 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

Citations29
Published2013
Admission routes1
Has abstractyes

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