Seeing the rural healthcare journeys of older adults with atrial fibrillation through a photographic lens
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".