MétaCan
Menu
Back to cohort
Record W1988472938 · doi:10.1177/073346480001900407

Dementia and Food Exchange in Nursing Home Dining Areas

2000· article· en· W1988472938 on OpenAlexaboutno aff
Stephanie Silver, Steven M. Albert

Bibliographic record

VenueJournal of Applied Gerontology · 2000
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
Fundersnot available
KeywordsNursing homesDementiaMedicineGerontologyQuarter (Canadian coin)Environmental healthNursingGeographyDisease

Abstract

fetched live from OpenAlex

Some investigators have reported that residents of nursing homes engage in food exchange during meal times, and that demented residents may be more likely to engage in such behaviors, usually taking food as part of more general disinhibited behavior. We investigated this claim directly using a behavioral observation protocol. Over two 12-day periods, a total of 111 residents in two nursing home dining rooms was observed to determine the prevalence of food exchange. Food exchange behaviors were noted among demented and nondemented residents. Overall, the prevalence of such behaviors was low; less than 10% of residents were observed taking or giving food. Demented residents were somewhat more likely to take food than nondemented residents; however, these differences did not achieve statistical significance. Dining room staff intercepted food exchanges in only about one quarter of such cases.

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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.040
GPT teacher head0.368
Teacher spread0.329 · 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

Citations1
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Applied GerontologySame topicGeriatric Care and Nursing HomesFrench-language works237,207