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Record W2527475392 · doi:10.1177/1742395316670462

Seeing the rural healthcare journeys of older adults with atrial fibrillation through a photographic lens

2016· article· en· W2527475392 on OpenAlexafffund
Kathy L. Rush, Nelly D. Oelke, Matt Shay, Chloe Pedersen

Bibliographic record

VenueChronic Illness · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of CalgaryUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsFocus groupHealth careNarrativeQualitative researchMedicineFace (sociological concept)Narrative inquiryRural areaGerontologyPsychologySociologyPolitical sciencePathology

Abstract

fetched live from OpenAlex

Objectives Obtaining the voices of older adult patients with atrial fibrillation (AF) about their health and healthcare has become increasingly important in providing quality care. The purpose of this study was to understand the unique contributions of photographs in the healthcare journeys of rural living older adults with AF. Methods As part of a larger mixed methods study 10 older adults with AF living in rural communities were recruited through two rural primary care physicians' practices. They were followed over six months through a combination of face-to-face and telephone interviews. Photographs were submitted along with personal journey logs to report healthcare interactions. A photographic analysis was conducted. Results Collectively photos illuminated aspects of older adults AF journeys (stable, chronic unstable, and acute crisis) less explicit in the narrative accounts. Three themes emerged: focus of attention, life-space, and support. Shifts in illness as a focus of attention and life-space paralleled patients at different points in their AF journeys while a range of formal and informal supports were available to them. Discussion Photographs were valuable in shedding light on older adults' rural healthcare experiences. They offer a nuanced approach for gaining insights into the subtleties characterizing the journeys of older adults with AF.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 head0.121
GPT teacher head0.360
Teacher spread0.239 · 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 routes2
Has abstractyes

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