The meaning of being an oncology nurse: Investing to make a difference
Bibliographic record
Abstract
The landscape of cancer care is evolving. Oncology nursing continues to develop and respond to the changing needs of patients with cancer and their families. There is limited understanding of what it means to be an oncology nurse, as well as the factors that facilitate or hinder being an oncology nurse. This study used an interpretive phenomenological approach. Six nurses from two in-patient units in a tertiary care teaching facility were interviewed. The overarching theme, Investing to Make a Difference, reflected how oncology nurses invested in building relationships with patients and their family members and invested in themselves by developing their knowledge and skills and, eventually, their identities as oncology nurses. In turn, these investments enhanced their role, and were seen to make a difference in the lives of patients and their family members by supporting them through the cancer journey. Implications of these findings for oncology nursing are highlighted as they relate to nursing practice, education, research, and leadership.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.047 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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".