Beyond the realist turn: a socio‐material analysis of heart failure self‐care
Bibliographic record
Abstract
For patients living with chronic illnesses, self-care has been linked with positive outcomes such as decreased hospitalisation, longer lifespan, and improved quality of life. However, despite calls for more and better self-care interventions, behaviour change trials have repeatedly fallen short on demonstrating effectiveness. The literature on heart failure (HF) stands as a case in point, and a growing body of HF studies advocate realist approaches to self-care research and policymaking. We label this trend the 'realist turn' in HF self-care. Realist evaluation and realist interventions emphasise that the relationship between self-care interventions and positive health outcomes is not fixed, but contingent on social context. This paper argues socio-materiality offers a productive framework to expand on the idea of social context in realist accounts of HF self-care. This study draws on 10 interviews as well as researcher reflections from a larger study exploring health care teams for patients with advanced HF. Leveraging insights from actor-network theory (ANT), this study provides two rich narratives about the contextual factors that influence HF self-care. These descriptions portray not self-care contexts but self-care assemblages, which we discuss in light of socio-materiality.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".