Bibliographic record
Abstract
The delivery of cancer care in Ontario is facing unprecedented challenges. Shortages in nursing, as in all professional disciplines, are having an impact on the delivery of cancer care. Oncology nurses have a major role to play in the delivery of optimum cancer care. Oncology nursing, when adequately defined and supported, can benefit the cancer delivery system, patients, and families. A primary nursing model is seen as being key to the delivery of optimum cancer care. Primary nursing as a philosophy facilitates continuity of care, coordination of a patient's care plan, and a meaningful ongoing relationship with the patient and his/her family. Primary nursing, when delivered in the collaboration of a nurse-physician team, allows for medical resources to be used appropriately. Defined roles enable nurses to manage patients within their scope of practice in collaboration with physicians. Enacting other nursing roles, such as nurse practitioners and advanced practice nurses, can also enable the health care system to manage a broader number of patients with more complex needs. This article presents a position paper originally written as the basis for an advocacy and education initiative in Ontario. It is shared in anticipation that the work may be useful to oncology nurses in other jurisdictions in their efforts to advance oncology nursing and improvement of patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".