Adaptive practices in heart failure care teams: implications for patient-centered care in the context of complexity
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF), one of the three leading causes of death, is a chronic, progressive, incurable disease. There is growing support for integration of palliative care's holistic approach to suffering, but insufficient understanding of how this would happen in the complex team context of HF care. This study examined how HF care teams, as defined by patients, work together to provide care to patients with advanced disease. METHODS: Team members were identified by each participating patient, generating team sampling units (TSUs) for each patient. Drawn from five study sites in three Canadian provinces, our dataset consists of 209 interviews from 50 TSUs. Drawing on a theoretical framing of HF teams as complex adaptive systems (CAS), interviews were analyzed using the constant comparative method associated with constructivist grounded theory. RESULTS: This paper centers on the dominant theme of system practices, how HF care delivery is reported to work organizationally, socially, and practically, and describes two subthemes: "the way things work around here", which were commonplace, routine ways of doing things, and "the way we make things work around here", which were more conscious, effortful adaptations to usual practice in response to emergent needs. An adaptive practice, often a small alteration to routine, could have amplified effects beyond those intended by the innovating team member and could extend to other settings. CONCLUSION: Adaptive practices emerged unpredictably and were variably experienced by team members. Our study offers an empirically grounded explanation of how HF care teams self-organize and how adaptive practices emerge from nonlinear interdependencies among diverse agents. We use these insights to reframe the question of palliative care integration, to ask how best to foster palliative care-aligned adaptive practices in HF care. This work has implications for health care's growing challenge of providing care to those with chronic medical illness in complex, team-based settings.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".