Patient perceptions of helpful communication in the context of advanced cancer
Bibliographic record
Abstract
AIMS AND OBJECTIVES: Based on a secondary analysis of data from a large qualitative study on cancer care communication, we address the question: what do patients with advanced cancer identify as helpful in their communication encounters with health care providers? BACKGROUND: Communication is of critical importance to the care of patients with advanced cancer. A better understanding of what such patients identify as helpful in their communication encounters with nurses and other health care providers seems critical to creating evidence-informed recommendations for best practices. DESIGN: Secondary analysis of qualitative interview data. METHODS: Data from 18 participants interviewed individually and 16 focus group participants, with advanced cancer in the palliative phase of care. Interpretive description methodology informed data collection and analysis. RESULTS: Findings suggest four key elements are critically important to consider in communications with patients in an advanced or palliative phase - respecting the importance of time, demonstrating caring, acknowledging fear and balancing hope and honesty in the provision of information. CONCLUSIONS: Communication is an important element in the provision of advanced cancer care. RELEVANCE TO CLINICAL PRACTICE: Findings emphasise the complex meanings inherent in cancer care communication and identify central themes that are fundamental to effective cancer care communication.
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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 | 0.017 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".