Advanced practice nurse-led early palliative care: a novel model for improved access to care
Bibliographic record
Abstract
BACKGROUND: Although the benefits of early palliative care have been established in advanced cancers, there remains a lack of access to and poor uptake of these services. Barriers include healthcare provider attitudes, lack of standardized referral approach, misaligned policy limited resources, and lack of palliative care expertise. PURPOSE: In consideration of these barriers, a description of the development and implementation of an early palliative care initiative at CancerCare Manitoba in Canada is presented. Explanations of innovative planning, processes, teams, and interventions are offered to facilitate insight for those intending to offer early palliative care. DISCUSSION: Barriers and facilitators to integrating a nurse-led early palliative care initiative are discussed. Considerations to mitigate common barriers encountered and leveraging existing resources are offered. CONCLUSIONS: This information may be beneficial to those working to facilitate access to palliative care in oncology for patients with advanced cancers. This model has been successful at our institution with pre and post implementation data presented in an upcoming publication.
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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 | 0.007 | 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 | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".