The 2003 Helene Hudson Memorial Lecture: Oncology nursing: Our past is the prologue... Can we author the future?
Bibliographic record
Abstract
The development of the standards of care, roles in oncology nursing, and role competencies was an opportunity for Canadian nurses to revisit their professional roots, review and validate their present roles, and revise or reaffirm their future roles. The standards of care for individuals with cancer and their families affirmed the centrality of the individual and family in any nursing interaction and gave voice to the stated needs of Canadians at risk for, or living with cancer. For the first time in Canada, a specialty nursing organization has taken the lead to clearly define contemporary nursing roles and competencies. This new vision has captured the interest of oncology nurses. CANO, the nursing profession, other health care professionals, and health care decision-makers must now also consider how this enlightened view of oncology nursing can be operationalized. The presenters have had the opportunity to "hear" the stories of Canadian oncology nurses and their experiences in striving for excellence in their practice. These stories highlight the evidence supporting the standards of oncology nursing. In addition, the challenges and facilitators for achieving excellence in oncology nursing practice are identified. Through these stories, the future for the development of oncology practice and operationalization of the new standards of care emerges.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".