How family physicians introduce palliative care to patients with chronic illnesses
Bibliographic record
Abstract
OBJECTIVE: Increasing numbers of Canadians living with complex, life-limiting conditions demand high-quality palliative care. Timely access to palliative care can help to reduce stress, improve quality of life, and provide relief for patients and their families. The purpose of this study is to explore the experiences of family physicians (FPs) regarding the decision and process of introducing palliative care to patients with chronic diseases. METHODS: Interpretive description methodology was used to guide the investigation of the research question. Thirteen Calgary Zone FPs participated in individual interviews. Data was collected iteratively and analyzed using constant comparative analysis. RESULTS: Analysis of interviews identified the overarching themes of dignity and empowerment, which describe the experience of FPs introducing palliative care to chronically ill patients. Four subthemes were woven throughout, including the art of conversation, therapeutic relationships, timing, and preparation of the patient and family. DISCUSSION: While the benefits of palliative conversations are widely accepted, a deeper understanding of how FPs can be supported in developing this aspect of their practice is needed. Understanding their experience provides knowledge that can serve as a framework for future education, mentorship, and competency development.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".