Exploring patient awareness of palliative care - optimal timing and preferred approaches
Bibliographic record
Abstract
OBJECTIVES: To explore patients' awareness levels of palliative care (PC) and how this awareness shapes their preferences regarding the timing and approach for discussing it. METHODS: The study, conducted at a prominent institution specializing in oncology care, enrolled women aged 18-75 years who had been diagnosed with breast cancer. Patients completed guiding questions: Do you know what PC is?, When is the most appropriate time and the most appropriate way to discuss PC?. The interviews were conducted exclusively via video call and were recorded, transcribed, and then deleted. RESULTS: The study involved 61 participants, averaging 49 years old. Almost half (47.5%) had completed high school. Qualitative data analysis revealed 9 thematic categories. Regarding the first question, 2 divergent categories emerged: care for life and threatening treatment. For the second question, opinions diverged into 4 categories: At an early stage, mid-course of the disease, as late as possible, and no time at all. For the third question, 3 categories emerged: communication and support, care setting and environment, and improving the PC experience. SIGNIFICANCE OF RESULTS: This study reveals diverse perspectives on patients' awareness and preferences for discussing PC, challenging the misconception that it's only for end-of-life (EOL) situations. Comprehending PC influences when and how patients discuss it. If tied solely to EOL scenarios, discussions may be delayed. Conversely, understanding its role in enhancing advance support encourages earlier conversations. Limited awareness might delay talks, while informed patients actively contribute to shared decision-making. Some patients prefered early involvement, others find mid-treatment discussions stress-relieving. Community support, quiet environments, and accessible resources, underscoring the importance of a calm, empathetic approach, emphasizing the importance of understanding its role in advance support and providing valuable implications for enhancing patient care practices, theories, and policies.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".