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Record W3157650032 · doi:10.1186/s12877-021-02235-5

Health care transitions for persons living with dementia and their caregivers

2021· article· en· W3157650032 on OpenAlexafffundabout
Jessica Ashbourne, Véronique Boscart, Samantha B. Meyer, Catherine Tong, Paul Stolee

Bibliographic record

VenueBMC Geriatrics · 2021
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsConestoga CollegeUniversity of Waterloo
FundersUniversity of WaterlooAlzheimer Society
KeywordsDementiaConstructivist grounded theoryDyadMedicineGerontologyContext (archaeology)NursingHealth careGrounded theoryQualitative researchQuality of life (healthcare)PsychologyDiseaseDevelopmental psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Persons with dementia are likely to require care from various health care providers in multiple care settings, necessitating navigation through an often-fragmented care system. This study aimed to create a better understanding of care transition experiences from the perspectives of persons living with dementia and their caregivers in Ontario, Canada, through the development of a theoretical framework. METHODS: Constructivist grounded theory guided the study. Seventeen individual caregiver interviews, and 12 dyad interviews including persons with dementia and their caregivers, were recorded and transcribed verbatim. The data were coded using NVivo 10 software; analysis occurred iteratively until saturation was reached. RESULTS: A theoretical framework outlining the context, processes, and influencing factors of care transitions was developed and refined. Gaining an in-depth understanding of the complex care transitions of individuals with dementia and their caregivers is an important step in improving the quality of care and life for this population. CONCLUSION: The framework developed in this study provides a focal point for efforts to improve the health care transitions of persons living with 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 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 score0.245
Threshold uncertainty score0.325

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.019
GPT teacher head0.289
Teacher spread0.270 · 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

Citations45
Published2021
Admission routes3
Has abstractyes

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