Developing a Service Model That Integrates Palliative Care Throughout Cancer Care: The Time Is Now
Bibliographic record
Abstract
Palliative care is a fundamental component of cancer care. As part of the 2011 to 2012 Leadership Development Program (LDP) of the American Society of Clinical Oncology (ASCO), a group of participants was charged with advising ASCO on how to develop a service model integrating palliative care throughout the continuum of cancer care. This article presents the findings of the LDP group. The group focused on the process of palliative care delivery in the oncology setting. We identified key elements for models of palliative care in various settings to be potentially equitable, sustainable, feasible, and acceptable, and here we describe a dynamic model for the integrated, simultaneous implementation of palliative care into oncology practice. We also discuss critical considerations to better integrate palliative care into oncology, including raising consciousness and educating both providers and the public about the importance of palliative care; coordinating palliative care efforts through strengthening affiliations and/or developing new partnerships; prospectively evaluating the impact of palliative care on patient and provider satisfaction, quality improvement, and cost savings; and ensuring sustainability through adequate reimbursement and incentives, including linkage of performance data to quality indicators, and coordination with training efforts and maintenance of certification requirements for providers. In light of these findings, we believe the confluence of increasing importance of incorporation of palliative care education in oncology education, emphasis on value-based care, growing use of technology, and potential cost savings makes developing and incorporating palliative care into current service models a meaningful goal.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".