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Record W2139799082 · doi:10.1111/scs.12176

A recipe for mealtime resilience for families living with dementia

2014· article· en· W2139799082 on OpenAlexafffund
Fiona Wong, Heather Keller, Lori Schindel Martin, Olga Sutherland

Bibliographic record

VenueScandinavian Journal of Caring Sciences · 2014
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsInstitute of AgingToronto Metropolitan UniversityResearch Institute for AgingUniversity of WaterlooUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of CanadaAlzheimer Society
KeywordsRecipeResilience (materials science)DementiaPsychologyGerontologyMedicineHistory

Abstract

fetched live from OpenAlex

To date, research delving into the narratives of persons living with dementia is limited. Taking part in usual mealtime activities such as preparing food can sustain the identity of persons living with dementia. Yet if capacity for mealtime activities changes, this can put a strain or demand on the family, which must adjust and adapt to these changes. The aim of this study was to develop an in-depth story of resilience in one family living with dementia that was experiencing mealtime changes. Thematic narrative analysis following the elements of Clandinin and Connelly's (2000) 3D narrative inquiry space was used. One family's dementia journey was highlighted using the metaphor of a baking recipe to reflect their story of resilience. Developing positive strategies and continuing to learn and adapt were the two approaches used by this resilient family. Reminiscing, incorporating humour, having hope and optimism, and establishing social support were specific strategies. This family continued to learn and adapt by focusing on their positive gains and personal growth, accumulating life experiences, and balancing past pleasures while adapting to the new normal. Future work needs to further conceptualise resilience and how it can be supported in families living with dementia.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.311
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.368
Teacher spread0.339 · 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 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

Citations4
Published2014
Admission routes2
Has abstractyes

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