Contribution of the pivot nurse in oncology to the experience of receiving a diagnosis of cancer by the patient and their loved ones
Bibliographic record
Abstract
The announcement of a cancer diagnosis represents a difficult situation for the patient, their loved ones and professionals (Reich, Vennin & Belkacémie, 2008). Until now, few studies have described nurses' contribution to this critical moment along the care trajectory (Tobin, 2012) and even fewer, the contribution of the pivot nurse in oncology (OPN) or infirmière pivot en oncologie (PNO) as this specialist is called in Quebec. This study aims to document the OPN's contribution to the cancer experience of the patient and their loved ones, from the time the diagnosis is communicated to the period immediately following (four to six weeks). Fourteen PNOs from a Montreal university health centre took part in two individual interviews. Results show that PNOs offer personalized support which draws on their expertise to better understand the experience lived by patients and their loved ones, and adapt their interventions according to their needs and the timing of these interventions. These results support issuing three recommendations for nursing practice in the areas of PNOs; development of expertise, interprofessional collaboration and environment.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".