Staffing a Specialist Palliative Care Service, a Team-Based Approach: Expert Consensus White Paper
Bibliographic record
Abstract
Palliative care is an evolving field with extensive studies demonstrating its benefits to patients, families, and the health care system. Many health systems have developed or are developing palliative care programs. The Canadian Society of Palliative Care Physicians (CSPCP) is often asked to recommend how many palliative care specialists are needed to implement and support an integrated palliative care program. This information would allow health service decision makers and educational institutions to plan resources accordingly to manage the needs of their communities. The CSPCP is well positioned to answer this question, as many of its members are Directors of palliative care programs and have been responsible for creating and overseeing the pioneering work of building these programs over the past few decades. In 2017, the CSPCP commissioned a working group to develop a staffing model for specialist palliative care teams based on the interdependence of three key professional roles, an extensive literature search, key stakeholder interviews, and expert opinions. This article is the Canadian Society of Palliative Care's recommended starting point that will be further evaluated as it is utilized across Canada. For more information and to see sample calculations go to the Canadian Society of Palliative Care Physicians Staffing Model for Palliative Care Programs (https://www.cspcp.ca).
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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 | 0.197 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".