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Record W2024052835 · doi:10.4061/2010/906818

Methods to Enhance Verbal Communication between Individuals with Alzheimer's Disease and Their Formal and Informal Caregivers: A Systematic Review

2010· review· en· W2024052835 on OpenAlexafffund
Mary Egan, Daniel Bérubé, Geneviève Racine, Carol Léonard, Elizabeth Rochon

Bibliographic record

VenueInternational Journal of Alzheimer s Disease · 2010
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity of British ColumbiaUniversity of Ottawa
FundersToronto Rehabilitation Institute
KeywordsCINAHLDementiaMedicineDiseaseInclusion (mineral)Psychological interventionIntervention (counseling)MEDLINEGerontologySystematic reviewFamily caregiversClinical psychologyPsychiatryPsychologyPathology

Abstract

fetched live from OpenAlex

Alzheimer's disease is the leading cause of dementia in older adults. Although memory problems are the most characteristic symptom of this disorder, many individuals also experience progressive problems with communication. This systematic review investigates the effectiveness of methods to improve the verbal communication of individuals with Alzheimer's disease with their caregivers. The following databases were reviewed: PsychINFO, CINAHL, EMBASE, MEDLINE, REHABDATA, and COMDIS. The inclusion criteria were: (i) experimentally based studies, (ii) quantitative results, (iii) intervention aimed at improving verbal communication of the affected individual with a caregiver, and (iv) at least 50% of the sample having a confirmed diagnosis of Alzheimer's disease. A total of 13 studies met all of the inclusion criteria. One technique emerged as potentially effective: the use of memory aids combined with specific caregiver training programs. The strength of this evidence was restricted by methodological limitations of the studies. Both adoption of and further research on these interventions are recommended.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.889
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.043
GPT teacher head0.426
Teacher spread0.383 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations108
Published2010
Admission routes2
Has abstractyes

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