‘Who is on your health‐care team?’ Asking individuals with heart failure about care team membership and roles
Bibliographic record
Abstract
BACKGROUND: Complex, chronically ill patients require interprofessional teams to address their multiple health needs; heart failure (HF) is an iconic example of this growing problem. While patients are the common denominator in interprofessional care teams, patients have not explicitly informed our understanding of team composition and function. Their perspectives are crucial for improving quality, patient-centred care. OBJECTIVES: To explore how individuals with HF conceptualize their care team, and perceive team members' roles. SETTING AND PARTICIPANTS: Individuals with advanced HF were recruited from five cities in three Canadian provinces. DESIGN: Individuals were asked to identify their HF care team during semi-structured interviews. Team members' titles and roles, quotes pertaining to team composition and function, and frailty criteria were extracted and analysed using descriptive statistics and content analysis. RESULTS: A total of 62 individuals with HF identified 2-19 team members. Caregivers, nurses, family physicians and cardiologists were frequently identified; teams also included dentists, foot care specialists, drivers, housekeepers and spiritual advisors. Most individuals met frailty criteria and described participating in self-management. DISCUSSION: Individuals with HF perceived being active participants, not passive recipients, of care. They identified teams that were larger and more diverse than traditional biomedical conceptualizations. However, the nature and importance of team members' roles varied according to needs, relationships and context. Patients' degree of agency was negotiated within this context, causing multiple, sometimes conflicting, responses. CONCLUSION: Ignoring the patient's role on the care team may contribute to fragmented care. However, understanding the team through the patient's lens - and collaborating meaningfully among identified team members - may improve health-care delivery.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".