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Record W2568289537 · doi:10.7748/ns.2017.e10460

Using drawings as a reflective tool to enhance communication in dementia care

2017· article· en· W2568289537 on OpenAlexfundno aff
Phil McEvoy, Sue Bellass

Bibliographic record

VenueNursing Standard · 2017
Typearticle
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsnot available
FundersAlzheimer Society
KeywordsDementiaPacePsychologyFocus (optics)Nonverbal communicationPsychotherapistDevelopmental psychologyCognitive psychologyMedicineDisease

Abstract

fetched live from OpenAlex

Communication skills training can be a valuable means of supporting professional and family carers of people with dementia. Most communication skills training programmes for those caring for people with dementia focus on dementia awareness and the technical aspects of communication, such as the pace and volume of the carer's speech. However, it is also important to examine what is conveyed about a carer's internal experience in their non-verbal interactions with people living with dementia. This article explores how drawings can be used to help carers to reflect on what is communicated and question any hidden assumptions. It discusses three case studies to demonstrate the complex dynamics that may be involved in interactions with people with dementia: the loss of shared memories, facing towards someone with dementia rather than away from them, and talking about issues that may be upsetting. Drawings provide a means for carers to access their unspoken thoughts and emotions, and can help them to improve their understanding of non-verbal interactions with people who have 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 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)0.015
metaresearch head score (Gemma)0.059
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.059
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.001
Science and technology studies0.0020.003
Scholarly communication0.0040.004
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.031
GPT teacher head0.436
Teacher spread0.405 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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