For Annals of Palliative Medicine special series—Oncology Nursing
Bibliographic record
Abstract
When we were asked to serve as Guest Editors for a special series focused on Oncology Nursing, we eagerly accepted opportunity.Both of us had been involved in collaborations in many corners of the world with nurses who cared for cancer patients and we were in awe of the development in oncology nursing which had been unfolding, often despite real hardship and challenge.Nurses around the globe had been working diligently to ensure care for people at risk of cancer or with cancer and their caregivers was the best that it could be.The nurses often identified the need for improvement and with colleagues, were keen to action.Yet for many, barriers and seemingly insurmountable struggles were their daily reality.As Guest Editors, we felt a special series focused on oncology nursing around the world would provide a picture of the reality.This allowed us the opportunity to connect with colleagues, who may not yet have had an opportunity to share their stories.Nonetheless, we thought it prudent to review developments in education, research, leadership, and advocacy, given these have been the main routes to growing the specialty.These papers may pave the way for nurses in various settings, determining how they might progress the recognition and growth of oncology nursing as a specialty in their own countries.Thus, this special series gives insight into the perspectives of nurses who care for cancer patients in various regions of the world.Clearly, there are fundamental differences from setting to setting in economics, health beliefs, educational systems, and healthcare approaches.All of these factors influence how nurses are able to function and develop specialty practices in their ever-changing healthcare systems and circumstances.Undoubtedly, what strategies work in one setting may or may not be the ones to use in another setting.However, by sharing the approaches nurses around the globe have utilised, this special series offers a range of potential approaches.What is common among the narratives within the articles is the perseverance and determination of nurses, making concerted efforts through the years to highlight the concerns of cancer patients and their families.Their successes are closely tied to access to education, engagement in research and evidence-based practice, and their capacity to participate in policy development.The creation and establishment of a professional organization for cancer nurses, in-country, regional and global, has been key to drawing nurses together and fostering networking, professional development, and a shared sense of purpose.Colleagues who were invited to write for this special series expressed a desire to ensure the current status of oncology nursing was reflected.It has been several decades since the seeds of the speciality were planted and the growth has been remarkable.This is a testament to the vision and stamina of nurses working in adult and paediatric cancer care.Their efforts over the years have led to significant changes in care practices for patients and families as well as the evidence to support those practices.Symptom management, active listening, patient education, patient and caregiver support, patient empowerment, resilience, and leadership lie at the heart of compassionate nursing; all factors are evident throughout the special series.We hope the special series will serve to describe the state of the oncology nursing specialty in 2023 whilst providing the vision to grow together throughout the globe.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.139 | 0.061 |
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".