The Impact of Interlink Community Cancer Nurses on the Experience of Living With Cancer
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To describe the impact of expert oncology nursing support provided by an independent nursing service, Interlink Community Cancer Nurses, on patients' experience of living with cancer. DESIGN: Qualitative research design guided by hermeneutic phenomenology using a broad data-generating statement. SETTING: Homes of participants in a metropolitan city. SAMPLE: Purposive sampling was used to select participants for the study. Eighteen women and two men with a variety of cancer diagnoses and living circumstances participated in the study. Accrual was stopped when data saturation occurred. MAIN RESEARCH VARIABLES: Patients' perceptions of the experience of receiving care in the home setting from expert oncology nurses. FINDINGS: Seven core themes described the participants' perceptions of and the impact of expert oncology nursing care on their experience of living with cancer. CONCLUSIONS: Expert oncology nursing support in the community is perceived by people living with cancer as having a significant impact on their experience and influencing their well-being and survival. The knowledge and experience of oncology nurses and the way in which care is delivered are critical elements. Further research should continue to explore the relationship between expert community oncology nursing support and healthcare outcomes for people with cancer. IMPLICATIONS FOR NURSING: Community nursing agencies need to examine their ability to ensure access to knowledgeable and experienced oncology nurses who can support and address the needs of people with cancer across the cancer 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".