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Brazilian transcultural adaptation of an instrument on communicative strategies of caregivers of elderly with dementia

2017· article· en· W2761273783 on OpenAlexaboutno aff
Lais Lopes Delfino, Ricardo Shoiti Komatsu, Caroline Komatsu, Anita Liberalesso Néri, Meire Cachioni

Bibliographic record

VenueDementia & Neuropsychologia · 2017
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsPortugueseBrazilian PortugueseDementiaPsychologyAdaptation (eye)Context (archaeology)Scale (ratio)ComprehensionLinguisticsMedicineHistoryCartographyGeography

Abstract

fetched live from OpenAlex

Communication with patients with dementia may be a difficult task for caregivers. OBJECTIVE: The aim of this study was to produce a Brazilian transcultural adaptation of an instrument developed in Canada, called the Small Communication Strategies Scale, composed of 10 items constructed from 10 communicative strategies most recurrent in a literature survey. METHODS: Drawing on understanding of the construction of the Small Communication Strategies Scale (SCSS), a Brazilian Portuguese version of the instrument was devised through the following steps: translation, back-translation and semantic-cultural adaptation by a specialized linguist in English-Portuguese translations and application of the comprehension test for the version produced in a group of caregivers of elderly individuals with dementia. RESULTS: The transcultural equivalence process was performed and two items of the SCSS needed adapting to the Brazilian context. After changes suggested by a specialized linguist, the final version was applied to 34 caregivers and the transcultural equivalence considered satisfactory. CONCLUSION: The Brazilian version of the instrument was successfully transculturally adapted for future validation and application in Brazil.

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 score0.353
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.341
Teacher spread0.299 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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