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Record W2154312059 · doi:10.1080/21551197.2011.591270

Healthy Eating Perceptions of Older Adults Living in Canadian Rural and Northern Communities

2011· article· en· W2154312059 on OpenAlexaffabout
Virginia M. Krahn, Christina Lengyel, Pam Hawranik

Bibliographic record

VenueJournal of Nutrition in Gerontology and Geriatrics · 2011
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsAthabasca UniversityUniversity of Manitoba
Fundersnot available
KeywordsFocus groupMedicineGerontologySocioeconomic statusRural areaAffect (linguistics)PerceptionQualitative researchEnvironmental healthPopulationPsychology

Abstract

fetched live from OpenAlex

Aging produces physiologic changes that can affect the nutritional health of the older adult. It is estimated that 80% of community-dwelling older adults have inadequate intakes of four or more nutrients. Socioeconomic factors, such as income and geographic location, can also play an important role in nutritional status; however, limited research is available that specifically explores this. The purpose of this qualitative study was to examine the healthy eating perceptions of older adults residing in rural and northern communities in one Canadian province. Five focus groups were conducted in three rural and two northern Manitoba communities. Thirty-nine older adults participated in audio-recorded focus groups. Five themes emerged from the discussions. All respondents stated that healthy eating was important, but knowledge deficits were observed regarding label reading, understanding and visualizing portion sizes, and vitamin D recommendations and sources. Food programs were not commonly attended by participants due to availably and resistance. Regularly delivered nutrition education programs would assist in providing current nutrition information to older adults and their families in rural settings.

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 score0.365
Threshold uncertainty score0.949

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 head0.035
GPT teacher head0.309
Teacher spread0.274 · 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

Citations14
Published2011
Admission routes2
Has abstractyes

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