Gaining a better understanding of the support function of oncology nurse navigators from their own perspective and that of people living with cancer: Part 2
Bibliographic record
Abstract
Individuals living with cancer have a wide range of needs throughout the disease trajectory. To better meet them, the PQLC [French acronym of the Quebec Cancer Control Program] implemented the oncology nurse navigator (ONN) role. A first article presented the nature of the needs of individuals living with cancer and of the support provided by ONNs. This second article aims at gaining a better understanding of the ONN support function from the ONNs' own perspective and to complete an exploratory description of their support interventions for individuals living with cancer and their loved ones throughout the care trajectory. The sample includes ten ONNs. They reported doing support interventions for all the various types of needs, especially in the informational (44%) and practical areas such as coordinating appointments for exams, practitioners and various services (35%). The results also suggest that a needs assessment is a prerequisite to intervention. Lastly, all participants underscored the importance of the helping relation and trusting relationship to clarify their support function. These results are consistent with the findings of several recent articles on the psychosocial role of oncology nurses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".