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Record W4211068098 · doi:10.3928/19404921-20220131-01

Willingness to Participate in Clinical Research Among Individuals With Cognitive Impairment

2022· article· en· W4211068098 on OpenAlexaboutno aff
Mengchi Li, Hyejin Kim, Susan M. Sereika, Trevor J. Nissley, Jennifer H. Lingler

Bibliographic record

VenueResearch in Gerontological Nursing · 2022
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
FundersNational Institute on Aging
KeywordsConcordanceCognitionHealth carePsychologyDementiaClinical psychologyAnosognosiaNeuropsychologyLogistic regressionMontreal Cognitive AssessmentCognitive impairmentMedicineDiseaseGerontologyPsychiatry

Abstract

fetched live from OpenAlex

This secondary analysis examined (1) factors associated with willingness to participate in clinical research for cognitive health among individuals with cognitive impairment and their care partners, and (2) concordance regarding such willingness between individuals with cognitive impairment and their care partners (dyads). Neuropsychological factors and willingness to participate in clinical research were collected using self-reported questionnaires. Participants' sociodemographic and clinical information was extracted from the University of Pittsburgh Alzheimer's Disease Research Center record. Binary logistic regression and Cohen's kappa coefficient analyses were performed. Greater trust in medical researchers ( p = 0.031, B = 0.127) and more severe cognitive impairment ( p = 0.009, B = −0.289) were associated with willingness to participate in clinical research among individuals with cognitive impairment. Dyadic agreement on willingness to have the individual with cognitive impairment enroll in clinical research was poor to fair (κ = 0.380). Findings suggest that individuals with cognitive impairment with greater trust in health professionals are more likely to agree to clinical research participation. Nurses and other health care providers who counsel individuals with cognitive impairment and their care partners should work to build trusting relationships with participants and be mindful of how increased trust can alter power dynamics between participants and health care professionals. [ Research in Gerontological Nursing, 15 (2), 76–84.]

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)0.014
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.676
GPT teacher head0.644
Teacher spread0.032 · 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.

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

Citations15
Published2022
Admission routes1
Has abstractyes

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