MétaCan
Menu
Back to cohort
Record W2086924230 · doi:10.1080/21551197.2013.840257

Construct Validation and Test–Retest Reliability of a Mealtime Satisfaction Questionnaire for Retirement Home Residents

2013· article· en· W2086924230 on OpenAlexaff
Lisa Pizzola, Zoe Martos, Kaylen J. Pfisterer, C.P.G.M. de Groot, Heather Keller

Bibliographic record

VenueJournal of Nutrition in Gerontology and Geriatrics · 2013
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of WaterlooUniversity of Guelph
Fundersnot available
KeywordsCronbach's alphaMedicineConstruct validityReliability (semiconductor)Internal consistencyTest (biology)Intraclass correlationQuality of life (healthcare)GerontologyConstruct (python library)Clinical psychologyPsychometricsNursing

Abstract

fetched live from OpenAlex

Mealtime satisfaction is an important component of quality of life (QOL) in residential care, yet there currently is no self-administered tool described in the literature. The purpose of this study is to investigate internal and test-retest reliability, and construct validity of a mealtime satisfaction questionnaire (MSQ) designed for residential care, more specifically retirement homes. A 15-item MSQ was developed and eligible participants from four retirement homes (n = 749) were invited to participate. The participation rate was 24% and the median age was 88 years for respondents. The internal consistency of the MSQ was high (Cronbach Alpha = 0.83) and the test-retest reliability was also high (Intraclass coefficient = 0.91, P < 0.01). The MSQ was associated with a valid and reliable QOL instrument for older adults (Mann Whitney Test = 1595.5, P < 0.01). The MSQ is reliable and is content and construct valid. QOL can be enriched by improving mealtime satisfaction in retirement homes.

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)0.007
metaresearch head score (Gemma)0.012
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 score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.028
GPT teacher head0.328
Teacher spread0.300 · 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
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Nutrition in Gerontology and GeriatricsSame topicNutrition and Health in AgingFrench-language works237,207