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Record W2278197869 · doi:10.1177/1471301216635827

Innovative practice: Conversational use of English in bilingual adults with dementia

2016· article· en· W2278197869 on OpenAlexaff
Kristina M. Kokorelias, Ellen B. Ryan, Gail Elliot

Bibliographic record

VenueDementia · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Methods and Practices
Canadian institutionsMcMaster UniversityWestern University
Fundersnot available
KeywordsDementiaConversationPsychologyVocabularyFirst languageNeuroscience of multilingualismLinguisticsMedicineCommunicationDisease

Abstract

fetched live from OpenAlex

Regression to mother tongue is common in those with dementia. In two long-term care facilities, we explored the use of bilinguals' two languages for five older adults with mild-moderate dementia who have begun to regress to Greek. We also examined the role of Montessori DementiAbility Methods: The Montessori Way-based English language activities in fostering conversational use of English. Over 10 sessions, participants' vocabulary or grammatical structure in English did not improve. However, four of the five participants were able to maintain a conversation in English for longer periods of time. This study contributes to strategies for optimizing meaningful conversation for bilingual long-term care residents with dementia. Moreover, the data suggest a change in the policy and practice for dementia care so that there are more opportunities for residents to speak English in non-English mother-tongue facilities. Greater attention to the specific language needs of bilinguals in English-dominant settings would also be advisable.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.051
GPT teacher head0.389
Teacher spread0.338 · 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 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

Citations7
Published2016
Admission routes1
Has abstractyes

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