Exposing barriers to end-of-life communication in heart failure: an integrative review.
Bibliographic record
Abstract
BACKGROUND: End-of-life (EOL) communication is lacking despite patients with heart failure (HF) and their caregivers desiring it. AIM: To review the existing literature to identify barriers that inhibit EOL communication in the HF population. METHOD: We chose an integrative literature review method and began by searching CINAHL, Medline, PsychInfo, Web of Science, Health Source Nursing Academic, Evidence-Based Medicine Reviews (EBMR), dissertations and theses searches through the University of Victoria and through Proquest from 1995 to 2011. DATA EVALUATION: EOL communication regarding wishes, prognosis and options for care rarely happen. We noted that patients lacked understanding of HF, feared engaging health care professionals (HCP), did not wish to talk about EOL, or waited for HCPs to initiate the conversation. HCPs lacked communication skills, focused on curative therapies and found diagnosing and prognosticating HF difficult. Limited time and space for conversations played a role. CONCLUSION: The challenge of diagnosing and prognosticating HF, its unpredictable trajectory, HCP inexperience in recognizing nearing EOL and lack of communication skills lead to HCPs avoiding EOL conversations. Four categories of barriers to communication were identified: patient/caregiver, HCP, disease-specific and organizational challenges.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".