Barriers to Goals of Care Discussions with Hospitalized Patients with Advanced Heart Failure: Feasibility and Performance of a Novel Questionnaire
Bibliographic record
Abstract
AIMS: Good end-of-life communication and decision-making are important to patients with advanced heart failure (HF) and their families, but their needs remain unmet. In this pilot study, we describe the feasibility and performance of a novel questionnaire aimed at identifying barriers and solutions to improve communication and decision-making about goals of care for hospitalized patients with advanced HF. METHODS: We distributed questionnaires to staff cardiologists, cardiology trainees, and cardiology nurses who provide care for HF patients at a Canadian teaching hospital. The questionnaire asked about the importance of various barriers to goals of care discussions. It also asked participants to rank their willingness to engage in goals of care discussions and their views on other clinicians could engage in such discussions. RESULTS: Of 76 clinicians, 44 (58%) completed the questionnaire (median completion time, 17 min). Individual survey questions had few missing responses (0% to 2%) for questions about barriers to goals of care discussions. There was appreciable discrimination of the importance of different barriers (mean scores 2.2 to 6.0 on a 7-point scale). Preliminary data suggest that clinicians perceive patient and family factors, such as difficulty accepting a poor prognosis, as the most important barriers preventing goals of care discussions. CONCLUSIONS: In this pilot study, we have demonstrated the feasibility of a novel questionnaire to be used in a larger multi-centre study of end-of-life HF care. Essential information will be obtained to inform the design and evaluation of interventions that seek to improve communication and decision-making about goals of care with HF patients.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".