The “Pivot” Nurse in Pediatric Transplantation: An Innovative Role in the Team
Bibliographic record
Abstract
It is well known and largely described in the litterature that an hematopoietic stem cell transplantation (HSCT) is an extremely stressful experience for the child and his/her family. The enormous amount of person from every discipline (medical, nursing and other professionals) that needs to interact with the families across the care pathway of the transplantation makes it difficult for most of them to completely understand what is happening to their child. In order to help the families “survive” this ordeal, we have developed an innovative nursing role that is essential in the process. The role of the “pivot” nurse can be compare in many ways to the role of the nurse navigator. The role has mainly five mandates: 1) evaluate the psychosocial needs of the families, 2) teach and inform the families about the transplant process, 3) support the families by having therapeutic relationships in order to empower them, 4) coordinate all the cares in the pathway (including the patients from reference centers) and lead the interdisciplinary team and meetings and 5) administrate the outpatient cares (central line, treatments, etc…). Apart from that, they are involve in nursing research and FACT accreditation. In our team, we have two “pivot” nurses that works in the outpatient clinic. The main advantage of this innovative role is a global support of the child and his/her family with always the same person which makes it easier and more efficient for everyone. The “pivot” nurse knows perfectly well her patients, the step they have achieve in the treatments, their needs and their coping mechanisms. On the other side, the families can develop a trusting relationship with her and participate more easily in the various aspects of the treatment. She can be easily reach in case of need or emergency by the families or any member of the transplant team or the reference centers. For all those reasons, we believe that this role has simplified and optimize the follow up of our patients, specially those coming from reference centers across the country, and has certainly consolidate the quality of care given to our patients.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".