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Record W2006064901 · doi:10.1177/153331750001500511

Communication between individuals with dementia and their caregivers during activities of daily living

2000· article· en· W2006064901 on OpenAlexaff
Jeff Small, Kathy Geldart, Gloria Gutman

Bibliographic record

VenueAmerican Journal of Alzheimer s Disease & Other Dementias® · 2000
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsDementiaContext (archaeology)Focus groupPsychologyActivities of daily livingFamily caregiversDevelopmental psychologyApplied psychologyClinical psychologyGerontologyMedicineDiseasePsychiatry

Abstract

fetched live from OpenAlex

Much previous research has focused on linguistic factors that can lead to communication breakdown in caregiver-patient interactions. However, the impact of such linguistic deficits on communication may vary depending on the context, goals, and complexity of the interaction. As a result, the likelihood of experiencing communication problems is expected to differ across different activities. In the present study, family caregivers of persons with dementia were asked to discuss communication challenges that they have experienced in a range of daily activities in the home. Four focus groups, involving a total of 22 caregivers, were conducted in community settings. The main goal of the focus groups was to identify specific daily activities in the home in which caregivers most often experience communication problems. The content of the focus groups was audio recorded and transcribed, and then coded and analyzed using qualitative and quantitative analytic techniques. The analyses focused on identifying trends across caregivers in which particular activities were noted as being prone to communication breakdown at different stages of the disease. Information about which activities are most communicatively challenging should assist caregivers in preparing for and adapting to these changes.

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)0.000
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 score0.028
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.055
GPT teacher head0.336
Teacher spread0.281 · 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

Citations75
Published2000
Admission routes1
Has abstractyes

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