Gaining a better understanding of the support function of oncology nurse navigators from their own perspective and that of people living with cancer: Part 1
Bibliographic record
Abstract
Individuals living with cancer have a wide range of needs throughout the disease trajectory. To better meet them, the Quebec Cancer Control Program (PQLC) implemented the oncology nurse navigator role. While this practitioner has already been integrated into the majority of oncology teams, the role still lacks precision when it comes to its functions within care teams. The support function that deals indiscriminately with "the full spectrum of care and services" consolidated under the larger constructs of adaptation and rehabilitation provided to the individual with cancer and their loved ones requires professional skills and organizational resources, which would improve with clarifying. The goal of this study is to better understand the oncology nurse navigator (ONN) support function, first from the perspective of individuals living with cancer and, second, from the perspective of ONNs themselves. The first objective, detailed in this first part of two, is to explore, from the perspective of people living with cancer, the nature of their needs and support provided by the ONN along the disease trajectory. In all, five individuals living with cancer and provided with an ONN were recruited. The participants expressed support needs at all levels regarding the ONN particularly in the emotional (56%) and informational areas. Moreover results suggest that symptom management (physical area) and all-around coordination (care interventions, appointments, exams, practitioners) in the practical area are paramount throughout the care trajectory.
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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 | 0.003 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".