A Multifaceted Approach to Spreading Palliative Care Consultation Services in California Public Hospital Systems
Bibliographic record
Abstract
Historically, California's 17 public hospital systems-those that are county owned and operated, and those University of California medical centers with the mandate to serve low income, vulnerable populations-have struggled to implement Palliative Care Consultation Services (PCCS)-this, despite demonstrated need for these services among the uninsured and Medicaid populations served by these facilities. Since 2008, through a collaborative effort of a foundation, a palliative care training center, and a nonprofit quality improvement organization, the Spreading Palliative Care in Public Hospitals initiative (SPCPH) has resulted in a 3-fold increase in the number of California public hospitals providing PCCS, from 4 to 12. The SPCPH leveraged grant funding, the trusted relationships between California public hospitals and their quality improvement organization, technical assistance and training, peer support and learning, and a tailored business case demonstrating the financial/resource utilization benefits of dedicated PCCS. This article describes the SPCPH's distinctive design, features of the public hospital PCCS, patient and team characteristics, and PCCS provider perceptions of environmental factors, and SPCPH features that promoted or impeded their success. Lessons learned may have implications for other hospital systems undertaking implementation of palliative care services.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 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".