Can a complex adaptive systems perspective support the resiliency of the heart failure patient – informal caregiver dyad?
Bibliographic record
Abstract
PURPOSE OF REVIEW: A holistic palliative approach for heart failure care emphasizes supporting nonprofessional informal caregivers. Informal caregivers play a vital role caring for heart failure patients. However, caregiving negatively affects informal caregivers' well being, and in turn heart failure patients' health outcomes. This opinion article proposes that complex adaptive systems (CAS) theory applied to heart failure models of care can support the resiliency of the heart failure patient - informal caregiver dyad. RECENT FINDINGS: Heart failure care is enacted within a complex system composed of patients, their informal caregivers and a variety of health professionals. In a national study, we employed a CAS perspective to explore how all parts of the heart failure team function interdependently in emergent and adaptive ways. Salient in our data were the severe vulnerability of elderly heart failure patients and their long-term partners who suffered from a chronic illness. Novel approaches are needed that can quickly adapt and reorganize care when unpredictable disturbances occur in the couples' functional capacity. SUMMARY: The linear protocol-driven care models that shape heart failure guidelines, training and care delivery initiatives do not adequately capture heart failure patients' social environment. CAS is a powerful theoretical tool that can render visible the most vulnerable members of the heart failure team, and incite robust specialized holistic palliative heart failure care models.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".