The oncology nurse in population cancer screening - Expanding the navigator role.
Bibliographic record
Abstract
Emerging trends in oncology nursing literature indicate an increasing need for specialized roles. The increasing complexity of cancer care presents a unique opportunity to expand the cancer patient navigator role to population cancer screening programs. Existing oncology nurse navigation competencies can be leveraged to improve cancer screening participation and reduce waiting times for access to diagnostic and treatment pathways. By expanding the scope of practice for oncology nurse navigators to include roles such as community engagement and improved access to care, nurse navigators in cancer screening programs can play a pivotal role in reducing cancer burden and improve health outcomes at a population level. In 2019, Newfoundland and Labrador's (NL) Provincial Cancer Care Program introduced a Screening Navigator position within its Population Screening Programs and specified community engagement and outreach as key requirements within the role. In 2021, a second screening navigator position was added. This article will highlight the expanded responsibilities of oncology nurse navigators in NL's Population Screening Program to facilitate improved participant access, continuity of screening and diagnostic pathways and community engagement. By expanding the navigator scope to include cancer screening, community engagement, and improved access to care, oncology nurse navigators play a key role in reducing cancer burden and improve health outcomes at a population level.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".