Nurse Navigation and the Transition to Cancer Survivorship: A Review of Determinants Essential to Program Success
Bibliographic record
Abstract
<div class="page" title="Page 26"><div class="layoutArea"><div class="column"><p><span>Nurse navigation programs are becoming prominent in the field of cancer care. As a newly emerging field, nurse navigation employs nurses and other health care profes- sionals who assist patients in overcoming barriers throughout the cancer continuum. The concept of nurse navigation is being extended to focus on survivorship, which is described as the period following active cancer treatment where patients often en- counter barriers affecting their care and quality of life. By utilizing specific skills and modalities, including education, communication, and coordination, survivorship navigators are able to assist in reducing disparities such as knowledge and communi- cation inadequacies, thus, facilitating optimal access to survivorship care. Access to health services is an important determinant of health in Canada. Survivorship navi- gation programs incorporate health services, providing a method in which cancer patients can overcome challenges and improve their health outcomes. This review will discuss the origins of nurse navigation, highlight navigator skills and modalities, which are essential to program success, and finally discuss the implications of a sur- vivorship navigation program. </span></p></div></div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".