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Record W4234586423 · doi:10.32920/ryerson.14653482

Serving up mealtime strategies : how families experience dementia in the community

2021· preprint· en· W4234586423 on OpenAlexaff
Abigail Wickson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDementiaPsychologyContext (archaeology)Set (abstract data type)Variety (cybernetics)Developmental psychologyGerontologyMedicineDisease

Abstract

fetched live from OpenAlex

The mealtime experiences for people with dementia and their caregivers living in the community has not been extensively explored. An existing data set provided information on the mealtime strategies used to cope with changing dementia behaviours. A secondary analysis of data from 10 dyads of people with dementia and their caregivers were analyzed. Four categories were identified including: Strategies to facilitate eating; Strategies to promote a sense of self; Stategies to minimize risk; and Strategies to promote caregiver well-being. The dyads used a variety of strategies that were common to all stages of dementia; however by the late stages, the dyads used more specific strategies. In general, the mealtime strategies used by adult caregivers and spousal caregivers did not greatly differ but rather the context in which they engaged in mealtimes did. The results demonstrated that there are opportunities to educate families and professionals about potential mealtime strategies.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.146
GPT teacher head0.396
Teacher spread0.249 · 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 designQualitative
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

Citations0
Published2021
Admission routes1
Has abstractyes

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