Nurse Practitioner Care Delivery Models: Meeting the Rapidly Expanding Needs of Cancer Patients
Bibliographic record
Abstract
Half of all Canadians will develop cancer at some point in their lifetimes. These rates have increased substantially over the last decade alongside increasing effectiveness and complexity of treatment options. Therefore, the need for patients to receive both an early diagnosis and ongoing care has never been so important. In Alberta, referrals to oncology specialty care have increased 18% in the last 7 years with no commensurate increase in the number of oncology health care professionals. Challenges with oncologic care access and provider recruitment are not unique to Alberta. In 2004, Cancer Care Alberta, specifically the Cross Cancer Institute (CCI), embarked on an initiative focusing on nurse practitioner (NP) care provision, aiming to address these gaps. The purpose of this article is a description of four distinct NP care models: the Assigned model, Consultative model, Partner model, and Most Responsible Provider (MRP model) significantly contributing to enhanced and expanded cancer care delivery at CCI. To the best of our knowledge, we are the first to demonstrate how NPs can significantly address the rapidly expanding demands for specialist oncology care. This work highlights roles and exemplars of NP care to meet the evolving needs of cancer patients, the multidisciplinary care team and the health system.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".