Engaging Family Physicians in the Provision of Palliative and End-of-Life Care: Can We Do Better?
Bibliographic record
Abstract
Background: Evidence shows the benefits of having a family physician (FP) at the heart of a care team that delivers palliative and end-of-life care (PEoLC). However, FPs have limitations on their ability to provide PEoLC. Objectives: We conducted a quality improvement study to (1) explore the barriers FPs encounter in providing PEoLC in our metropolitan context and (2) identify potential strategies to overcome these challenges. Methods: We interviewed a cohort of FPs from 10 different clinical practices within a metropolitan area (British Columbia [BC], Canada); this cohort is not regularly engaged with our Specialist Palliative Care Team. Verbatim transcripts were examined using inductive thematic analysis. Results: All FPs identified home visits as a critical aspect of being able to provide PEoLC. Despite this consensus, work-life balance, time, and compensation are major barriers to providing home visits and PEoLC. Local healthcare system awareness (available resources, why and how to access them) was identified as a barrier that can potentially be addressed through education sessions. Although 5 out of 10 FPs had not had formal palliative care education or training, clinical education was not considered a barrier to provide PEoLC. Conclusion: Providing FPs with tools and resources through education, including why and how to access them, and adjusting the BC compensation model to address home visit's travel time and time modifiers may better support FPs to provide PEoLC.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".