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Record W2946678652 · doi:10.1080/01621424.2019.1614505

Supporting older adults’ engagement in health-care programs and policies: Findings from a rural cognitive health study

2019· article· en· W2946678652 on OpenAlexafffundabout
Juanita-Dawne Bacsu, Thomas McIntosh, Marc Viger, Shanthi Johnson, Bonnie Jeffery, Nuelle Novik

Bibliographic record

VenueHome Health Care Services Quarterly · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of AlbertaUniversity of SaskatchewanSaskatchewan HealthUniversity of Regina
FundersAlzheimer SocietySaskatchewan Health Research Foundation
KeywordsOutreachGeneral partnershipNewspaperSchema (genetic algorithms)Rural healthParticipant observationHealth carePublic relationsPsychologyGerontologyRural areaNursingMedical educationMedicineSociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Although rural seniors are important users of health-care services, their perspectives and input remain largely absent from health programs and policies. This article explores rural seniors' perspectives to support their engagement in patient-oriented research. Guided by lay theory and cultural schema theory, participant observation, concept maps, and semi-structured interviews were conducted with 42 rural seniors in Saskatchewan, Canada. Three themes were identified: community outreach through trust and partnership-building; using flexible data collection methods such as moving to open-ended interviews rather than closed-ended surveys; and developing community-relevant dissemination strategies such as local newspaper articles, posters, and community workshops. In moving forward, collaborative research with seniors is essential to improving health programs and policies for older adults in rural communities and beyond.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
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 head0.088
GPT teacher head0.544
Teacher spread0.456 · 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 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

Citations13
Published2019
Admission routes3
Has abstractyes

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