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Record W2218290350 · doi:10.3148/cjdpr-2015-025

Construct Validation of Three Nutrition Questions Using Health and Diet Ratings in Older Canadian Males Living in the Community

2015· article· en· W2218290350 on OpenAlexafffundvenueabout
Usman Akhtar, Heather Keller, Robert B. Tate, Christina Lengyel

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2015
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of ManitobaResearch Institute for AgingUniversity of Waterloo
FundersResearch Institute for Aging, University of Waterloo
KeywordsConstruct validityConstruct (python library)GerontologyMedicinePsychologyRating scaleClinical psychologyPsychometricsDevelopmental psychology

Abstract

fetched live from OpenAlex

Brief nutrition screening tools are desired for research and practice. Seniors in the Community: Risk Evaluation for Eating and Nutrition (SCREEN-II, 14 items) and the abbreviated version SCREEN-II-AB (8 items) are valid and reliable nutrition screening tools for older adults. This exploratory study used a retrospective cross-sectional design to determine the construct validity of a subset of 3 items (weight loss, appetite, and swallowing difficulty) currently on the SCREEN-II and SCREEN-II-AB tools. Secondary data on community-dwelling senior males (n = 522, mean ± SD age = 86.7 ± 3.0 years) in the Manitoba Follow-up Study (MFUS) study were available for analysis. Participants completed the mailed MFUS Nutrition Survey that included SCREEN-II items and questions pertaining to self-rated health, diet healthiness, and rating of the importance of nutrition towards successful aging as the constructs for comparison. Self-perceived health status (F = 14.7, P < 0.001), diet healthiness (ρ = 0.17, P = 0.002) and the rating of nutrition's importance to aging (ρ = 0.10, P = 0.03) were correlated with the 3-item score. Inferences were consistent with associations between these construct variables and the full SCREEN-II. Three items from SCREEN-II and SCREEN-II-AB demonstrate initial construct validity with self-perceived health status and diet healthiness ratings by older males; further exploration for criterion and predictive validity in more diverse samples is needed.

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)0.006
metaresearch head score (Gemma)0.010
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 score0.254
Threshold uncertainty score0.511

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.300
GPT teacher head0.480
Teacher spread0.180 · 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

Citations9
Published2015
Admission routes4
Has abstractyes

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