MétaCan
Menu
Back to cohort
Record W1965492017 · doi:10.3148/66.1.2005.5

<i>Mealtimes as Active Processes</i>in Long-term Care Facilities

2005· article· en· W1965492017 on OpenAlexaffvenueabout
Amie J. Gibbs-Ward, Heather Keller

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2005
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsLong-term careGrounded theoryPsychological interventionNursingGovernment (linguistics)PsychologyDementiaAffect (linguistics)GerontologyMedicineQualitative researchSociology

Abstract

fetched live from OpenAlex

Mealtimes are central to the nutritional care of residents in long-term care facilities. There has been little Canadian research to guide interdisciplinary practice around mealtimes. This study included a grounded theory approach to explore mealtime experiences of 20 people with dementia living in two long-term care facilities, and the meal-related care they received from registered nurses, health care aides, and dietitians. Theoretical sampling directed the collection and analysis of data from mealtime observations in special care units and key informant interviews with care providers. The constant comparison method was used to analyze and conceptualize the data. A substantive theory emerged with three key themes: 1. Each mealtime is a unique process embedded within a long-term care facility's environment. 2. Residents are central to the process through their actions (i.e., arriving, eating, waiting, socializing, leaving, and miscellaneous distracted activities). 3. Internal (i.e., residents' characteristics) and external (i.e., co-resident, direct caregiving, indirect caregiving, administrative, and government activities) influences affect residents' actions at mealtimes. The theory suggests that optimal mealtime experiences for residents require individualized care that reflects interdisciplinary, multi-level interventions.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 integrity0.0000.001
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 head0.084
GPT teacher head0.447
Teacher spread0.363 · 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 designNot applicable
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

Citations58
Published2005
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Dietetic Practice and ResearchSame topicNutrition and Health in AgingFrench-language works237,207