Ambulatory care unit role optimization for the specialized oncology nurse
Bibliographic record
Abstract
While the role of the specialized oncology nurse in treatment and symptom management is well established, the role in the outpatient setting is not as well defined. Increasing patient complexity, the rising incidence of cancer, and evolving treatment regimens is pressing BC Cancer to reassess its Ambulatory Care Unit (ACU) model of care to better meet patient needs. The purpose of this project was to identify and map the specific role and functional tasks of nurses working in the ACU to specialty competencies for the oncology nurse. A baseline functional role elements list and role-competency map were developed from clinical observations and focus group sessions. This work will help provide role clarity and enable nursing to articulate both the scope of practice and the specialty competencies required to best meet the needs of our patients in the ACU setting. The results of this project can be utilized in strategic and operational planning discussions focused on improving health services for patients and supporting the continued professional development of front-line staff. The approaches utilized may be of interest to others who wish to apply similar methods in their own cancer settings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".