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Record W2334113549

Exploring person-centered care and mealtimes for residents with dementia in specialized care units

2015· dissertation· en· W2334113549 on OpenAlexaboutno aff
Sarah Wu

Bibliographic record

VenueSummit (Simon Fraser University) · 2015
Typedissertation
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaNursingGerontologyMedicinePerson-centered carePsychologyHealth carePolitical scienceInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

This qualitative study explored the dining experience of residents with dementia living in specialized care units, with a focus on person-centered care aspects in staff practices.Data were collected by multiple methods: participant observation, informal and formal interviews with care staff, and examination of relevant documents at a long term care facility in British Columbia, Canada.Data analysis revealed five person-centered categories in relation to dining: (1) Relaxed Pace/ Outpacing, (2) Respect/ Disrespect, (3) Connecting/ Distancing, (4) Empowerment/ Disempowerment, and (5) Inclusion/ Ignoring.Analysis also identified the ways in which staff understood person-centered care, and factors affecting its delivery.These categories provide insight into the psychosocial and physical environmental factors that affect residents' dining experience.The findings suggest that while staff practices reflect several aspects of person-centered care, a biomedical undercurrent continues to hinder the evolution of dementia care.

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)0.005
metaresearch head score (Gemma)0.007
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 score0.009
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.006
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.112
GPT teacher head0.325
Teacher spread0.212 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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