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Record W2494456068 · doi:10.1108/jica-03-2016-0012

Risks and older adults with atrial fibrillation in rural communities: an integration lens

2016· article· en· W2494456068 on OpenAlexaff
Kathy L. Rush, Nelly D. Oelke, R. Colin Reid, Carol Laberge, Frank Halperin, Mary Kjorven

Bibliographic record

VenueJournal of Integrated Care · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsKelowna General HospitalOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsHealth carePsychological interventionFocus groupMedicinePopulation ageingService providerTeamworkPopulationNursingMedical emergencyBusinessService (business)Environmental healthMarketingEconomic growth

Abstract

fetched live from OpenAlex

Purpose – Older adults with atrial fibrillation (AF) have put growing demands on a poorly integrated healthcare system. This is of particular concern in rural communities with rapid population aging and few healthcare resources elevating risk of stroke and mortality. The purpose of this paper is to explore healthcare delivery risks for rural older adults with AF. Design/methodology/approach – This qualitative study collected data from AF patients, healthcare providers and decision makers. Ten patients participated in six-month care journeys involving interviews, logs, photos, and chart reviews. In total, 13 different patients and ten healthcare providers participated in focus groups and two decision makers participated in interviews. Findings – Three key health service risks emerged: lack of patient-focussed access and self-management; unplanned care coordination and follow-up across the continuum of care; and ineffective teamwork with variable perspectives among patients, providers, and decision makers. Originality/value – This study extends the understanding of risks to the health system level. Results provide important information for further research aimed at interventions to improve health service delivery and policy change to mitigate risks for this population.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.197
GPT teacher head0.389
Teacher spread0.192 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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