Exploring Health Care Providers' Experiences of Providing Collaborative Palliative Care for Patients With Advanced Heart Failure At Home: A Qualitative Study
Bibliographic record
Abstract
Background The HeartFull Collaborative is a regionally organized model of care which involves specialist palliative care and cardiology health care providers (HCPs) in a collaborative, home-based palliative care approach for patients with advanced heart failure (AHF). We evaluated HCP perspectives of barriers and facilitators to providing coordinated palliative care for patients with AHF at home. Methods and Results We conducted a qualitative study with 17 HCPs (11 palliative care and 6 cardiology) who were involved in the HeartFull Collaborative from April 2013 to March 2020. Individual, semi-structured interviews were held with each practitioner from November 2019 to March 2020. We used an interpretivist and inductive thematic analysis approach. We identified facilitators at 2 levels: (1) individual HCP level (on-going professional education to expand competency) and (2) interpersonal level (shared care between specialties, effective communication within the care team). Ongoing barriers were identified at 2 levels: (1) individual HCP level (e.g. apprehension of cardiology practitioners to introduce palliative care) and (2) system level (e.g. lack of availability of personal support worker hours). Conclusions Our results suggest that a collaborative shared model of care delivery between palliative care and cardiology improves knowledge exchange, collaboration and communication between specialties, and leads to more comprehensive patient care. Addressing ongoing barriers will help improve care delivery. Findings emphasize the acceptability of the program from a provider perspective, which is encouraging for future implementation. Further research is needed to improve prognostication, assess patient and caregiver perspectives regarding this model of care, and assess the economic feasibility and impact of this model of care.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| 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".