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Approaches to communication intervention in adults with neurodegenerative diseases

2025· book-chapter· en· W4415442965 on OpenAlexaff
Allison Chen, Marie Y. Savundranayagam, Angela C. Roberts

Bibliographic record

VenueOxford University Press eBooks · 2025
Typebook-chapter
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsPsychological interventionDementiaIntervention (counseling)Family caregiversHealth communicationQuality of life (healthcare)Leverage (statistics)

Abstract

fetched live from OpenAlex

Abstract The progression of communication difficulties in persons living with dementia is challenging for caregivers to navigate. Communication breakdowns can lead to increased responsive behaviors and decreased quality of care, along with increased caregiver burden and stress. This chapter reviews the literature on communication interventions for both family and formal caregivers of persons living with dementia. There is a growing body of literature on these interventions that demonstrates increased caregiver use of targeted communication strategies and increased caregiver knowledge of—and confidence in—communicating with a person living with dementia. There are gaps in the accessibility and scalability of communication interventions, particularly for family caregivers in underserved or remote areas. Future interventions should leverage technology to provide more personalized, scalable, and inclusive solutions. The chapter highlights the ongoing need for rigorously designed studies and implementation-focused research to ensure that communication interventions are effectively integrated into real-world healthcare and educational systems.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.002

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 head0.069
GPT teacher head0.242
Teacher spread0.172 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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