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Record W2953120681 · doi:10.1002/nop2.315

Exploring associations between older adults’ demographic characteristics and their perceptions of self‐care actions for communicating with healthcare professionals in southern United States

2019· article· en· W2953120681 on OpenAlexaff
Huey‐Ming Tzeng, Udoka Okpalauwaekwe, Cindy Feng, L. Jansen, Anne Barker, Chang‐Yi Yin

Bibliographic record

VenueNursing Open · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsLogistic regressionHealth careDescriptive statisticsPerceptionHealth professionalsPsychologyGerontologyOlder peopleCross-sectional studyMedicineFamily medicine

Abstract

fetched live from OpenAlex

AIMS: This study examined associations between older adults' demographic factors and their perceived importance of, desire to and ability to perform seven self-care behaviours for communicating with healthcare professionals. DESIGN: This cross-sectional survey study analysed subset data of 123 older adults 65 years and older, living in southern United States. METHODS: (57 items, grouped into 11 categories) was used to collect self-reported self-care data. Demographic characteristics were also collected. Descriptive statistics and logistic regression analyses were used to tests for relationships between the variables relevant to the research objective. RESULTS: Regression findings showed that separated older adults felt less able to share ideas about their healthcare experiences compared to married older adults. Male older adults reported less desire to list issues to discuss and less desire to share ideas about their care experience with their healthcare professionals compared to their female counterparts.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.278
GPT teacher head0.447
Teacher spread0.169 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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