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Record W3095833278 · doi:10.1177/0733464820970811

Optimizing the Meaningful Engagement of Older Adults With Multimorbidity and Their Caregivers as Research Partners: A Qualitative Study

2020· article· en· W3095833278 on OpenAlexafffund
Kristina Chang, Maureen Markle‐Reid, Ruta Valaitis, Carrie McAiney, Nancy Carter

Bibliographic record

VenueJournal of Applied Gerontology · 2020
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsUniversity of WaterlooMcMaster UniversityResearch Institute for AgingUniversity Health Network
FundersRegistered Nurses’ Foundation of OntarioCanada Research ChairsMcMaster University
KeywordsMultimorbidityPersonaQualitative researchFamily caregiversAging in placePsychologyGerontologyHealth careNursingMedicineComorbiditySociologyPsychiatry

Abstract

fetched live from OpenAlex

It is widely recognized that the engagement of older adults with multimorbidity and their caregivers as partners in health care research is important and invaluable. The objective of this study was to examine how researchers can best engage and support older adults with multimorbidity and informal friend or family caregivers of older adults with multimorbidity as research partners in health care research teams. The persona-scenario method was used for participants to create fictional stories. These stories were analyzed to shed light on specific strategies that can support older adults and caregivers as partners on health care research teams, such as a patient-centered approach, identifying and addressing barriers to engagement, and clarifying roles and responsibilities on the research team. The results from this study can be used to inform research, policy, and education on supporting older adults with multimorbidity and caregivers of older adults with multimorbidity as research partners.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.187
GPT teacher head0.442
Teacher spread0.256 · 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 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

Citations6
Published2020
Admission routes2
Has abstractyes

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