Strategies Used by Outpatient Oncology Nurses to Introduce Early Palliative Care
Bibliographic record
Abstract
BACKGROUND: Although early palliative care is linked to improved health-related quality of life, satisfaction with care, and symptom management, the clinical strategies that nurses use to actively initiate this care are unknown. OBJECTIVES: The aims of this study were to conceptualize the clinical strategies that outpatient oncology nurses use to introduce early palliative care and to determine how these strategies align with the framework of practice. METHODS: A constructivist-informed grounded theory study was conducted in a tertiary cancer care center in Toronto, Canada. Twenty nurses (6 staff nurses, 10 nurse practitioners, and 4 advanced practice nurses) from multiple outpatient oncology clinics (ie, breast, pancreatic, hematology) completed semistructured interviews. Analysis occurred concurrently with data collection and used constant comparison until theoretical saturation was reached. RESULTS: The overarching core category, pulling it all together , outlines the strategies used by oncology nurses to support timely palliative care referral, drawing on the coordinating, collaborating, relational, and advocacy dimensions of practice. The core category incorporated 3 subcategories: (1) catalyzing and facilitating synergy among disciplines and settings , (2) promoting and considering palliative care within patients' personal narratives , and (3) widening the focus from disease-focused treatment to living well with cancer . CONCLUSION: Outpatient oncology nurses enact unique clinical strategies, which are aligned with the nursing framework and reflected multiple dimensions of practice, to introduce early palliative care. IMPLICATIONS FOR PRACTICE: Our findings have clinical, educational, and policy implications for fostering the conditions in which nurses are supported to maximize their full potential in the introduction of early palliative care.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".